Visual saliency detection in colour images based on density estimation

International audience; A simple and effective method for visual saliency detection in colour images is presented. The method is based on the common observation that local salient regions exhibit distinct geometric and and texture patterns from neighbouring regions. We model the colour distribution of local image patches with a Gaussian density and measure the saliency of each patch as the statistical distance from that density. Experimental results with public datasets and comparison with other state-of-the-art methods show the effectiveness of our method.

Hyperspectral venous image quality assessment for optimum illumination range selection based on skin tone characteristics

Background Subcutaneous veins localization is usually performed manually by medical staff to find suitable vein to insert catheter for medication delivery or blood sample function. The rule of thumb is to find large and straight enough vein for the medication to flow inside of the selected blood vessel without any obstruction. The problem of peripheral difficult venous access arises when patient’s veins are not visible due to any reason like dark skin tone, presence of hair, high body fat or dehydrated condition, etc. Methods To enhance the visibility of veins, near infrared imaging systems is used to assist medical staff in veins localization process. Optimum illumination is crucial to obt…

On Spatio-Temporal Saliency Detection in Videos using Multilinear PCA

International audience; Visual saliency is an attention mechanism which helps to focus on regions of interest instead of processing the whole image or video data. Detecting salient objects in still images has been widely addressed in literature with several formulations and methods. However, visual saliency detection in videos has attracted little attention, although motion information is an important aspect of visual perception. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In this paper, we extend a recent saliency detection approach based on principal component analysis (PCA) which have shwon good results wh…

A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance

International audience; Prostate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentatio…

Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR

In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it into the loss function t…

Subcutaneous veins detection and backprojection method using Frangi vesselness filter

Blood vessels detection is a common task performed in numerous medical procedures. During regular medical treatments venipuncture procedures are performed for invasive medication and blood sampling. Near infrared imaging technology can be used to visualize the subcutaneous veins in cases of difficult venous access. In this paper the methods for veins centerline detection and back projection is presented. In order to highlight the suitable veins for venipuncture, the centerline of larger veins are detected and back projected to the original image. The method is applied on the near infrared images of subjects selected from four different classes of skin tone. This can be helpful to medical st…

Automatic Seed Placement for Breast Lesion Segmentation on US Images

Breast lesion boundaries have been mostly extracted by using conventional approaches as a previous step in the development of computer-aided diagnosis systems. Among these, region growing is a frequently used segmentation method. To make the segmentation completely automatic, most of the region growing methods incorporate automatic selection of the seed points. This paper proposes a new automatic seed placement algorithm for breast lesion segmentation on ultrasound images by means of assigning the probability of belonging to a lesion for every pixel depending on intensity, texture and geometrical constraints. The proposal has been evaluated using a set of sonographic breast images with acco…

Structural characterization and optical constants of p-toluene sulfonic acid doped polyaniline and its composites of chitosan and reduced graphene-oxide

Para-Toluene sulfonic acid doped polyaniline (PANI), PANI/chitosan composites, PANI/reduced graphene-oxide composites and a ternary composite comprising of PANI, chitosan and reduced graphene-oxide have synthesised via oxidative polymerisation of aniline by Ammonium peroxydisulfate (APS). FTIR, XRD, FESEM and UV-VIS techniques were performed for the confirmation of the successful synthesis. The fundamental optical parameters such as, complex refractive index, complex dielectric constants and optical conductivity of the PANI and the composites were investigated in the UV-VIS-NIR range. The results show a clear dependence on the constituent component such as sulphur as well as the absorbance …

Complex networks : application for texture characterization and classification

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

AUTOMATIC QUALITY ENHANCEMENT AND NERVE FIBRE LAYER ARTEFACTS REMOVAL IN RETINA FUNDUS IMAGES BY OFF AXIS IMAGING

International audience; Retinal fundus images acquired with non-mydriatic digital fundus cameras are a versatile tool for the diagnosis of various retinal diseases. Even with relative ease of use, the images produced sometimes suffer from reflectance artefacts mainly due to the nerve fibre layer (NFL) or camera lens related reflections. We propose a technique that employs multiple fundus images to obtain a single higher quality image without these reflectance artefacts, which also compensates for a suboptimal illumination. The removal of bright artefacts, can have great benefits for the reduction of false positives in the detection of retinal lesions by automatic systems or manual inspectio…

Investigation of Acetone Vapour Sensing Properties of a Ternary Composite of Doped Polyaniline, Reduced Graphene Oxide and Chitosan Using Surface Plasmon Resonance Biosensor

This work reports the use of a ternary composite that integrates p-Toluene sulfonic acid doped polyaniline (PANI), chitosan, and reduced graphene oxide (RGO) as the active sensing layer of a surface plasmon resonance (SPR) sensor. The SPR sensor is intended for application in the non-invasive monitoring and screening of diabetes through the detection of low concentrations of acetone vapour of less than or equal to 5 ppm, which falls within the range of breath acetone concentration in diabetic patients. The ternary composite film was spin-coated on a 50-nm-thick gold layer at 6000 rpm for 30 s. The structure, morphology and chemical composition of the ternary composite samples were character…

Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR

International audience; In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it …

A Review of Biosensors for Non-Invasive Diabetes Monitoring and Screening in Human Exhaled Breath

Exhaled breath acetone has been identified as a diabetes biomarker for non-invasive diagnosis. Its detection using biosensors features has many advantages over the conventional means. This paper reviews the recent literature on the detection of exhaled breath acetone and acetone vapor of diabetic interest. The biosensors have been classified based on their transduction methods. The performance characteristics of the biosensors have been explored for comparison. The future trends are also highlighted.

Wood fiber orientation assessment based on punctual laser beam excitation: A preliminary study

International audience; The EU imposes standards for the use of wood in structural applications. Local singularities such as knots affect the wood mechanical properties. They can be revealed by looking at the wood fiber orientation. For this reason, many methods were proposed to estimate the orientation of wood fiber using optical means, X-rays, or scattering measurement techniques. In this paper, an approach to assess the wood fiber orientation based on thermal ellipsometry is developed. The wood part is punctually heated with a Nd-YAG Laser and the thermal response is acquired by an infrared camera. The thermal response is elliptical due to the propagation of the heat through and along th…

Incorporating depth information into few-shot semantic segmentation

International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…

Extract information of polarization imaging from local matching stereo

Since polarization of light was used in the field of computer vision, the research of polarization vision is rapidly growing. Polarization vision has been shown to simplify some important image understanding tasks that can be more difficult to be performed with intensity vision. Furthermore, it has computational efficiency because it only needs grayscale images and can be easily applied by a simple optical setup. Nowadays, we can find various types of polarization cameras in the market. However, they are very expensive. In our work, we will study and develop a low price polarization camera setup with parallel acquisition using a stereo system. This system requires only two general cameras e…

Bright Retinal Lesions Detection using Color Fundus Images Containing Reflective Features

Recently, the research community has developed many techniques to detect and diagnose diabetic retinopathy with retinal fundus images. This is a necessary step for the implementation of a large scale screening effort in rural areas where ophthalmologists are not available. In the United States of America, the incidence of diabetes is increasing among the young population. Retina fundus images of patients younger than 20 years old present a high amount of reflectance due to the Nerve Fibre Layer (NFL). Generally, the younger the patient the more the reflectance is visible. We are not aware of algorithms able to explicitly deal with this type of artifact.

Probing large area surface plasmon interference in thin metal films using photon scanning tunneling microscopy.

Abstract The interference of surface plasmons can provide important information regarding the surface features of the hosting thin metal film. We present an investigation of the interference of optically excited surface plasmons in the Kretschmann configuration in the visible spectrum. Large area surface plasmon interference regions are generated at several wavelengths and imaged with the photon scanning tunneling microscope. Furthermore, we discuss the non-retarded dispersion relations for the surface plasmons in the probe–metal system modeled as confocal hyperboloids of revolution in the spheroidal coordinate systems.

Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation

The movement of patients and respiratory motion during MRI acquisition produce image artefacts that reduce the image quality and its diagnostic value. Quality assessment of the images is essential to minimize segmentation errors and avoid wrong clinical decisions in the downstream tasks. In this paper, we propose automatic multi-task learning (MTL) based classification model to detect cardiac MR images with different levels of motion artefact. We also develop an automatic segmentation model that leverages k-space based motion artefact augmentation (MAA) and a novel compound loss that utilizes Dice loss with a polynomial version of cross-entropy loss (PolyLoss) to robustly segment cardiac st…

Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-based method

We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boost- ing, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detect- ing anomalies under manufacturer production, as well as in classi- fying the anomalies among 20 listed categories. Manufacturer speci- fications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is ad- dressed by using a classification system relying on real-time ma- chine vision. To fulfill both real-time and quality constraints, three classification algorit…

MFCC-based Recurrent Neural Network for automatic clinical depression recognition and assessment from speech

Abstract Clinical depression or Major Depressive Disorder (MDD) is a common and serious medical illness. In this paper, a deep Recurrent Neural Network-based framework is presented to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, expanding training labels and transferred features are considered. The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accura…

Non-linear active disturbance rejection control for upper limb rehabilitation exoskeleton

Trajectory tracking in upper limb rehabilitation exercises is utilized for repeatability of joint movement to improve the patient’s recovery in the early stages of rehabilitation. In this article, non-linear active disturbance rejection control as a combination of non-linear extended-state observer and non-linear state error feedback is used for the sinusoidal trajectory tracking control of the two-link model of an upper limb rehabilitation exoskeleton. The two links represent movements like flexion/extension for both the shoulder joint and the elbow joint in the sagittal plane. The Euler–Lagrange method was employed to acquire a dynamic model of an upper limb rehabilitation exoskeleton. T…

A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images

Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi…

Dependence of the Optical Constant Parameters of p-Toluene Sulfonic Acid-Doped Polyaniline and Its Composites on Dispersion Solvents

The optical constants of Para-Toluene sulfonic acid-doped polyaniline (PANI), PANIchitosan composites, PANI-reduced graphene-oxide composites and a ternary composite comprising of PANI, chitosan and reduced graphene-oxide dispersed in diluted p-toluene sulfonic acid (PTSA) solution and N-Methyl-2-Pyrrolidone (NMP) solvent have been evaluated and compared. The optical constant values were extracted from the absorbance spectra of thin layers of the respective samples. The potential utilization of the materials as the active sensing materials of surface plasmon resonance biosensors has also been assessed in terms of the estimated value of the penetration depth through a dielectric medium. The …

A Survey of Prostate Segmentation Methodologies in Ultrasound, Magnetic Resonance and Computed Tomography Images

Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmenta…

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10years. This survey aims to provide a comprehen…

A 3D Scanner for Transparent Glass

Many practical tasks in industry, such as automatic inspection or robot vision, often require the scanning of three-dimensional shapes by use of non-contact techniques. However, few methods have been proposed to measure three-dimensional shapes of transparent objects because of the difficulty of dealing with transparency and specularity of the surface. This paper presents a 3D scanner for transparent glass objects based on Scanning From Heating (SFH), a new method that makes use of local surface heating and thermal imaging.

Video-Based Depression Detection Using Local Curvelet Binary Patterns in Pairwise Orthogonal Planes

International audience; Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Ort…

Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation

International audience; Low contrast of the prostate gland, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow regions, speckle and significant variations in prostate shape, size and in- ter dataset contrast in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a probabilistic framework for automatic initialization and propagation of multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. Unlike traditional statistical models of shape and int…

Classification of SD-OCT Volumes for DME Detection: An Anomaly Detection Approach

International audience; Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binar…

Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear cl…

Features extraction on complex images

The accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using microscopy. This technique allows the capture of a complex image (i.e., one containing magnitude and phase), and the reconstruction of the phase and magnitude information. Digital holograms give a new dimension to texture analysis, since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on the image segmentation of patterned wafers for defect detection. The paper presents a comparison between the features obtained using Gabor filtering on complex images under illumination and…

Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton.

Neurological disorders such as cerebral paralysis, spinal cord injuries[acronym](SCI), and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging [english]affectsaffect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the qual…

GESTALT-INSPIRED FEATURES EXTRACTION FOR OBJECT CATEGORY RECOGNITION

International audience; We propose a methodology inspired by Gestalt laws to ex- tract and combine features and we test it on the object cat- egory recognition problem. Gestalt is a psycho-visual the- ory of Perceptual Organization that aims to explain how vi- sual information is organized by our brain. We interpreted its laws of homogeneity and continuation in link with shape and color to devise new features beyond the classical proxim- ity and similarity laws. The shape of the object is analyzed based on its skeleton (good continuation) and as a measure of homogeneity, we propose self-similarity enclosed within shape computed at super-pixel level. Furthermore, we pro- pose a framework to …

Separating compound figures in journal articles to allow for subfigure classification

Journal images represent an important part of the knowledge stored in the medical literature. Figure classification has received much attention as the information of the image types can be used in a variety of contexts to focus image search and filter out unwanted information or ”noise”, for example non–clinical images. A major problem in figure classification is the fact that many figures in the biomedical literature are compound figures and do often contain more than a single figure type. Some journals do separate compound figures into several parts but many do not, thus requiring currently manual separation. In this work, a technique of compound figure separation is proposed and implemen…

Weighted Likelihood Function of Multiple Statistical Parameters to Retrieve 2D TRUS-MR Slice Correspondece for Prostate Biopsy

International audience; This paper presents a novel method to identify the 2D axial Magnetic Resonance (MR) slice from a pre-acquired MR prostate volume that closely corresponds to the 2D axial Transrectal Ultrasound (TRUS) slice obtained during prostate biopsy. The shape-context representations of the segmented prostate contours in both the imaging modalities are used to establish point correspondences using Bhattacharyya distance. Thereafter, Chi-square distance is used to find the prostate shape similarities between the MR slices and the TRUS slice. Normalized mutual information and correlation coefficient between the TRUS and MR slices are computed to find the information theoretic simi…

Microaneurysms detection with the radon cliff operator in retinal fundus images

ABSTRACT Diabetic Retinopathy (DR) is one of the leading causes of blindness in the industrialized world. Early detection is thekey in providing effective treatment. However, the current number of trained eye care specialists is inadequate to screenthe increasing number of diabetic patients. In recent years, automated and semi-automated systems to detect DR withcolor fundus images have been developedwith encouraging,but not fully satisfactory results. In this study we present theinitial results of a new techniquefor the detection and localization of microaneurysms,an early sign of DR. The algorithmis based on three steps: candidates selection, the actual microaneurysms detection and a Þnal …

A Performance Evaluation of Fusion Techniques for Spatio-Temporal Saliency Detection in Dynamic Scenes

International audience; Visual saliency is an important research topic in computer vision applications, which helps to focus on regions of interest instead of processing the whole image. Detecting visual saliency in still images has been widely addressed in literature. However, visual saliency detection in videos is more complicated due to additional temporal information. A spatio-temporal saliency map is usually obtained by the fusion of a static saliency map and a dynamic saliency map. The way both maps are fused plays a critical role in the accuracy of the spatio-temporal saliency map. In this paper, we evaluate the performances of different fusion techniques on a large and diverse datas…

Spectral clustering of shape and probability prior models for automatic prostate segmentation.

Imaging artifacts in Transrectal Ultrasound (TRUS) images and inter-patient variations in prostate shape and size challenge computer-aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose to use multiple mean parametric models derived from principal component analysis (PCA) of shape and posterior probability information to segment the prostate. In contrast to traditional statistical models of shape and intensity priors, we use posterior probability of the prostate region determined from random forest classification to build, initialize and propagate our model. Multiple mean models derived from spectral clustering of combined shape and appearance parameters…

Multi-Agent Based Energy Management in Microgrids Using MACSimJX

Excessive growth in electricity consumption has been experienced over the past few years due to an increase in population around the world.This tends to increase the use of renewable energy and randomness of the load. So.it is important to improve the traditional methodologies and techniques applied on microgrid to make it more intelligent. In this paper, multi agent system is employed over autonomous microgrid framework to endorse its intelligence. The Multi-Agent system is simulated in Java Agent Development Environment (JADE) environment and matlab toolbox Simulink is used for the implementation of the microgrid model. Further, MACSimJX is used to communicate between the micro grid and a…

Temperature imaging and image processing in the steel industry

Our aim is twofold: to present our temperature measurement system based on CCD technology, which gives a linear response versus temperature, and to display two industrial applications in which our system has been involved to optimize and characterize the process. We present a short summary dealing with temperature evaluations from radiation measurements. We consider especially the problems of the surroundings, the atmosphere, and the emissivity assumption. After selecting a value for the emissivity, we show that the use of the CCD technology enables us to obtain high spatial and temporal resolution temperature imaging, and provides further information, mainly a linear response versus temper…

“Scanning from Heating” and “Shape from Fluorescence”

3D surface acquisition is a subject which has been studied to a large extent. A significant number of techniques for acquiring shape have been proposed, and a wide range of commercial solutions are available. Nevertheless, today’s systems still have difficulties when digitizing objects with non-Lambertian surfaces in the visible light spectrum, as is the case of transparent, semi-transparent or highly reflective materials (e.g. glass, crystals, some plastics and shiny metals). In this chapter, some of the issues of traditional scanning systems are addressed by considering various approaches using the radioactive properties of materials, the polarization information of the reflected light as…

Texture Guided Active Appearance Model Propagation for Prostate Segmentation

Fusion of Magnetic Resonance Imaging (MRI) and Trans Rectal Ultra Sound (TRUS) images during TRUS guided prostate biopsy improves localization of the malignant tissues. Segmented prostate in TRUS and MRI improve registration accuracy and reduce computational cost of the procedure. However, accurate segmentation of the prostate in TRUS images can be a challenging task due to low signal to noise ratio, heterogeneous intensity distribution inside the prostate, and imaging artifacts like speckle noise and shadow. We propose to use texture features from approximation coefficients of Haar wavelet transform for propagation of a shape and appearance based statistical model to segment the prostate i…

Tackling the Problem of Data Imbalancing for Melanoma Classification

Comunicació de congrés presentada a: 3rd International Conference on Bioimaging, BIOIMAGING 2016 - Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016, Roma, Italy Malignant melanoma is the most dangerous type of skin cancer, yet melanoma is the most treatable kind of cancer when diagnosed at an early stage. In this regard, Computer-Aided Diagnosis systems based on machine learning have been developed to discern melanoma lesions from benign and dysplastic nevi in dermoscopic images. Similar to a large range of real world applications encountered in machine learning, melanoma classification faces the challenge of imbalanced data, where …

Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation

Automatic and accurate segmentation of the left atrial (LA) cavity and scar can be helpful for the diagnosis and prognosis of patients with atrial fibrillation. However, automating the segmentation can be difficult due to the poor image quality, variable LA shapes, and small discrete regions of LA scars. In this paper, we proposed a fully-automatic method to segment LA cavity and scar from Late Gadolinium Enhancement (LGE) MRIs. For the loss functions, we propose two different losses for each task. To enhance the segmentation of LA cavity from the multicenter dataset, we present a hybrid loss that leverages Dice loss with a polynomial version of cross-entropy loss (PolyCE). We also utilize …

IC‐P‐024: Localization of hippocampal atrophy in Alzheimer's disease

The hippocampus presents the highest rate of atrophy in the early stage of Alzheimer's disease (AD), with more pronounced neuron loss reported in CA1 and subiculum. The aim of this study is to increase the discrimination power of hippocampal shape analysis between AD and normal controls (NC) by focusing on the subregions with atrophy associated with AD and describing the localized shape changes using statistical shape models (SSMs).

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal d…

Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

In this paper, we present an evaluation of four encoder&ndash

A Shape-based Statistical Method to Retrieve 2D TRUS-MR Slice Correspondence for Prostate Biopsy

International audience; This paper presents a method based on shape-context and statistical measures to match interventional 2D Trans Rectal Ultrasound (TRUS) slice during prostate biopsy to a 2D Magnetic Resonance (MR) slice of a pre-acquired prostate volume. Accurate biopsy tissue sampling requires translation of the MR slice information on the TRUS guided biopsy slice. However, this translation or fusion requires the knowledge of the spatial position of the TRUS slice and this is only possible with the use of an electro-magnetic (EM) tracker attached to the TRUS probe. Since, the use of EM tracker is not common in clinical practice and 3D TRUS is not used during biopsy, we propose to per…

A spline-based non-linear diffeomorphism for multimodal prostate registration.

This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least…

Exudate Segmentation on Retinal Atlas Space

International audience; Diabetic macular edema is characterized by hard exudates. Presence of such exudates cause vision loss in the affected areas. We present a novel approach of segmenting exudates for screening and follow-ups by building an ethnicity based statistical atlas. The chromatic distribution in such an atlas gives a good measure of probability of the pixels belonging to the healthy retinal pigments or to the abnormalities (like lesions, imaging artifacts etc.) in the retinal fundus image. Post-processing schemes are introduced in this paper for the enhancement of the edges of such exudates for final segmentation and to separate lesion from false positives. A sensitivity(recall)…

Multiscale Attention-Based Prototypical Network For Few-Shot Semantic Segmentation

International audience; Deep learning-based image understanding techniques require a large number of labeled images for training. Few-shot semantic segmentation, on the contrary, aims at generalizing the segmentation ability of the model to new categories given only a few labeled samples. To tackle this problem, we propose a novel prototypical network (MAPnet) with multiscale feature attention. To fully exploit the representative features of target classes, we firstly extract rich contextual information of labeled support images via a multiscale feature enhancement module. The learned prototypes from support features provide further semantic guidance on the query image. Then we adaptively i…

A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images

International audience; Heterogeneous intensity distribution inside the prostate gland, significant variations in prostate shape, size, inter dataset contrast variations, and imaging artifacts like shadow regions and speckle in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a supervised learning schema based on random forest for automatic initialization and propagation of statistical shape and appearance model. Parametric representation of the statistical model of shape and appearance is derived from principal component analysis (PCA) of the probability distribution inside the prostate and PC…

A Coupled Schema of Probabilistic Atlas and Statistical Shape and Appearance Model for 3D Prostate Segmentation in MR Images

International audience; A hybrid framework of probabilistic atlas and statistical shape and appearance model (SSAM) is proposed to achieve 3D prostate segmentation. An initial 3D segmentation of the prostate is obtained by registering the probabilistic atlas to the test dataset with deformable Demons registration. The initial results obtained are used to initialize multiple SSAMs corresponding to the apex, central and base regions of the prostate gland to incorporate local variabilities. Multiple mean parametric models of shape and appearance are derived from principal component analysis of prior shape and intensity information of the prostate from the training data. The parameters are then…

Imaging standing surface plasmons by photon tunneling

We present a direct method for optically exciting and imaging delocalized standing surface plasmons in thin metal films. We show theoretically that when imaging the field of the plasmons with a photon scanning tunneling microscope, the presence of the dielectric probe has a negligible effect on the surface modes of the metal film. We demonstrate that plasmon interference can be sustained in arbitrarily large regions of the metal film in comparison to the excitation wavelength. This knowledge can be important when seeking the relative distance between two scattering centers such as the presence of micron or submicron structures.

SIFT Texture Description for Understanding Breast Ultrasound Images

Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images.

From Nowhere to Everywhere

International audience; This paper presents a synthetic view of a variety of projects built upon an Erasmuss Mundus Master Course. It highlights double degree programs, European credits transfer, joint PhDs, research collaborations as well as few other related European projects going from Thematic Networks to another Erasmus Mundus Course.

Deep Learning Techniques for Depression Assessment

Depression is a typical mood disorder, which affects a significant number of individuals worldwide at an increasing rate. Objective measures for early detection of signs related to depression could be beneficial for clinicians with regards to a decision support system. In this paper, assessment of depression is done by applying three deep learning techniques of Convolutional Neural Network (CNN). These techniques are transfer learning using AlexNet, fine-tuning using AlexNet and building an end to end CNN. The inputs of the CNNs are a combination of Motion History Image, Landmark Motion History Image and Gabor Motion History Image, and have been generated on a depression dataset. Accuracy o…

Real-time characterization of aspect flaws on warped surface by artificial vision

Artificial vision is an efficient means of assuring the quality of a certain class of products. The vision system must respect the industrial constraints, in particular, the production rate. The geometrical features of flaws are pertinent information used for the acceptance of the controlled product. This article presents a real-time algorithm for the geometrical characterization of defects located on warped objects. The algorithms described enable the characterization of defects by their size and their 2-D shape. Both parameters are calculated in real time by simple reference to a look-up table. The 2-D shape is obtained by a geometrical transform and an interpolation. The efficiency of th…

Motorcyclists Safety System to avoid Rear End Collisions based on Acoustic Signatures

International audience; In many Asian countries, motorcyclists have a higher fatality rate as compared to other vehicles. Among many other factors, rear end collisions are also contributing for these fatalities. Collision detection systems can be useful to minimize these accidents. However, the designing of efficient and cost effective collision detection system for motorcyclist is still a major challenge. In this paper, an acoustic information based, cost effective and efficient collision detection system is proposed for motorcycle applications. The proposed technique uses the Short time Fourier Transform (STFT) to extract the features from the audio signal and Principal component analysis…

Discrimination of retinal images containing bright lesions using sparse coded features and SVM

Diabetic Retinopathy (DR) is a chronic progressive disease of the retinal microvasculature which is among the major causes of vision loss in the world. The diagnosis of DR is based on the detection of retinal lesions such as microaneurysms, exudates and drusen in retinal images acquired by a fundus camera. However, bright lesions such as exudates and drusen share similar appearances while being signs of different diseases. Therefore, discriminating between different types of lesions is of interest for improving screening performances. In this paper, we propose to use sparse coding techniques for retinal images classification. In particular, we are interested in discriminating between retina…

Restoration of Videos Degraded by Local Isoplanatism Effects in the Near-Infrared Domain

When observing a scene horizontally at a long distance in the near-infrared domain, degradations due to atmospheric turbulence often occur. In our previous work, we presented two hybrid methods to restore videos degraded by such local perturbations. These restoration algorithms take advantages of a space-time Wiener filter and a space-time regularization by the Laplacian operator. Wiener and Laplacian regularization results are mixed differently depending on the distance between the current pixel and the nearest edge point. It was shown that a gradation between Wiener and Laplacian areas improves results quality, so that only the algorithm using a gradation will be used in this article. In …

Atlas selection strategy using least angle regression in multi-atlas segmentation propagation

International audience; In multi-atlas based segmentation propagation, segmentations from multiple atlases are propagated to the target image and combined to produce the segmentation result. Local weighted voting (LWV) method is a classifier fusion method which combines the propagated atlases weighted by local image similarity. We demonstrate that the segmentation accuracy using LWV improves as the number of atlases increases. Under this context, we show that introducing diversity in addition to image similarity by using least-angle regression (LAR) criteria is a more efficient way to rank and select atlases. The accuracy of multi-atlas segmentation converges faster when the atlases are sel…

A hybrid framework of multiple active appearance models and global registration for 3D prostate segmentation in MRI.

International audience; Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of the prostate reduces computational complexity and improves the multimodal registration accuracy. However, accurate and computationally efficient 3D segmentation of the prostate in MR images could be a challenging task due to inter-patient shape and intensity variability of the prostate gland. In this work, we propose to use multiple statistical shape and appearance models to segment the prostate in 2D and a global registration framework to impose shape restri…

A boosting approach for prostate cancer detection using multi-parametric MRI

International audience; Prostate cancer has been reported as the second most frequently diagnosed men cancers in the world. In the last decades, new imaging techniques based on MRI have been developed in order to improve the diagnosis task of radiologists. In practise, diagnosis can be affected by multiple factors reducing the chance to detect potential lesions. Computer-aided detection and computer-aided diagnosis have been designed to answer to these needs and provide help to radiologists in their daily duties. In this study, we proposed an automatic method to detect prostate cancer from a per voxel manner using 3T multi-parametric Magnetic Resonance Imaging (MRI) and a gradient boosting …

Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network

The number of prostate cancer cases is steadily increasing especially with rising number of ageing population. It is reported that 5-year relative survival rate for man with stage 1 prostate cancer is almost 99% hence, early detection will significantly improve treatment planning and increase survival rate. Magnetic resonance imaging (MRI) technique is a common imaging modality for diagnosis of prostate cancer. MRI provide good visualization of soft tissue and enable better lesion detection and staging of prostate cancer. The main challenge of prostate whole gland segmentation is due to blurry boundary of central gland (CG) and peripheral zone (PZ) which lead to differential diagnosis. Sinc…

A refined range image registration technique for multi-stripe laser scanner

Nowadays, visual inspection is very important in the quality control for many industrial applications. However, the complexity of most 3D objects constrains the registration of range images; a complete surface is required to compare the acquired surface to the model. Range finders are very used to digitalize free form shape objects with large resolutions. Moreover, one single view is not enough to reconstruct the whole surface due to occlusions, shadows, etc. In these situations, the motion between reconstructed partial views are required to integrate all surfaces in a single model. However, the use of positioning systems is not always available or adequate due mainly to the size of the obj…

Special Section Guest Editorial:Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems

This PDF file contains the editorial “Special Section Guest Editorial:Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems” for JEI Vol. 24 Issue 06

Optimization of a polarization imaging system for 3D measurements of transparent objects

This paper presents a multispectral imaging system for 3D reconstruction of transparent objects based on "shape from polarization" technique. The originality of this work relies on a multispectral active lighting system which enables to cope with the two ambiguities on the zenith angle and azimuth angle. A calibration step allows optimising the polarimetric measurements. Example of a reconstructed transparent object is presented.

Classification of Melanoma Lesions Using Sparse Coded Features and Random Forests

International audience; Malignant melanoma is the most dangerous type of skin cancer, yet it is the most treatable kind of cancer, conditioned by its early diagnosis which is a challenging task for clinicians and dermatologists. In this regard, CAD systems based on machine learning and image processing techniques are developed to differentiate melanoma lesions from benign and dysplastic nevi using dermoscopic images. Generally, these frameworks are composed of sequential processes: pre-processing, segmentation, and classification. This architecture faces mainly two challenges: (i) each process is complex with the need to tune a set of parameters, and is specific to a given dataset; (ii) the…

LOCALIZATION OF HIPPOCAMPAL ATROPHY IN ALZHEIMER'S DISEASE

International audience; The hippocampus presents the highest rate of atrophy in the early stage of Alzheimer's disease (AD), with more pronounced neuron loss reported in CA1 and subiculum. The aim of this study is to increase the discrimination power of hippocampal shape analysis between AD and normal controls (NC) by focusing on the subregions with atrophy associated with AD and describing the localized shape changes using statistical shape models (SSMs).

<title>Textures from stereo-based IR imaging for industrial tire inspection</title>

A conceptual system to produce 3D thermal models of tires for tire inspection and defect characterization is proposed. The system uses registered range and thermal information to build highly detailed 3D models using either a volumetric or mech-based approach. To achieve this goal, two narrow bandpass filters are used in conjunction with two IR cameras to obtain the true temperature of the target body. The thermal information is then translated to texture data and mapped as an overlay onto a 3D model. The textures are realizable through the use of three-component texture maps that include rgb values to specify the texture coordinates in the plane. The objective is to generate a movie loop d…

Video-Based Heartbeat Rate Measuring Method Using Ballistocardiography

International audience; Video-based heartbeat rate measurement is a rapidly growing application in remote health monitoring. Video-based heartbeat rate measuring methods operate mainly by estimating photoplethysmography or ballistocardiography signals. These methods operate by estimating the microscopic color change in the face or by estimating the microscopic rigid motion of the head/facial skin. However, the robustness to motion artifacts caused by illumination variance and motion variance of the subject poses main challenge. We present a video-based heartbeat rate measuring framework to overcome these problems by using the principle of ballistocardiography. In this paper, we proposed a b…

Statistical atlas based exudate segmentation

International audience; Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent referen…

Synthesis and characterisation of a ternary composite of polyaniline, reduced graphene-oxide and chitosan with reduced optical band gap and stable aqueous dispersibility

A ternary composite comprising of p-toluene sulfonic acid doped polyaniline (PANI), chitosan and reduced graphene oxide (RGO) with stable aqueous dispersibility has been synthesised via oxidative polymerisation of aniline in chitosan/RGO dispersion. For comparison; PANI, PANI/chitosan and PANI/RGO composites were also synthesised using the same procedure. FTIR, Raman, XPS, XRD and UV-VIS confirmed the successful synthesis of the PANI and the composites. The aqueous dispersions of the PANI/chitosan and the ternary composites were found to be stable even after more than four months. The stability of the dispersion was attributed to the polycationic nature of the chitosan. The thermogravimetri…

A new multimodal RGB and polarimetric image dataset for road scenes analysis

International audience; Road scene analysis is a fundamental task for both autonomous vehicles and ADAS systems. Nowadays, one can find autonomous vehicles that are able to properly detect objects present in the scene in good weather conditions but some improvements are left to be done when the visibility is altered. People claim that using some non conventional sensors (infra-red, Lidar, etc.) along with classical vision enhances road scene analysis but still when conditions are optimal. In this work, we present the improvements achieved using polarimetric imaging in the complex situation of adverse weather conditions. This rich modality is known for its ability to describe an object not o…

Validating retinal fundus image analysis algorithms: issues and a proposal.

This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running …

Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation

International audience; Accurate prostate segmentation in Trans Rectal Ultra Sound (TRUS) images is an important step in different clinical applications. However, the development of computer aided automatic prostate segmentation in TRUS images is a challenging task due to low contrast, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow, and speckle. Significant variations in prostate shape, size and contrast between the datasets pose further challenges to achieve an accurate segmentation. In this paper we propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from princi…

Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text

International audience; Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities ( audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements…

Active lighting applied to three-dimensional reconstruction of specular metallic surfaces by polarization imaging

International audience; In the field of industrial vision, the three-dimensional inspection of highly reflective metallic objects is still a delicate task. We deal with a new automated three-dimensional inspection system based on polarization analysis. We first present an extension of the shape-from-polarization method for dielectric surfaces to metallic surfaces. Then, we describe what we believe to be a new way of solving the ambiguity concerning the normal orientation with an active lighting system. Finally, applications to shape-defect detection are discussed, and the efficiency of the system to discriminate defects on specular metallic objects made by stamping and polishing is presente…

Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations,…

A Non-linear Diffeomorphic Framework for Prostate Multimodal Registration

International audience; This paper presents a novel method for non-rigid registration of prostate multimodal images based on a nonlinear framework. The parametric estimation of the non-linear diffeomorphism between the 2D fixed and moving images has its basis in solving a set of non-linear equations of thin-plate splines. The regularized bending energy of the thin-plate splines along with the localization error of established correspondences is jointly minimized with the fixed and transformed image difference; where, the transformed image is represented by the set of non-linear equations defined over the moving image. The traditional thin-plate splines with established correspondences may p…

Quality Control by Artificial Vision

This PDF file contains the editorial “Quality Control by Artificial Vision” for JEI Vol. 13 Issue 03

Gabor filtering for feature extraction on complex images: application to defect detection on semiconductors

AbstractThis paper is an extension of previous work on the image segmentation of electronic structures on patterned wafers to improve the defect detection process on optical inspection tools. Die-to-die wafer inspection is based upon the comparison of the same area on two neighbourhood dies. The dissimilarities between the images are a result of defects in this area of one of the dies. The noise level can vary from one structure to the other, within the same image. Therefore, segmentation is needed to create a mask and apply an optimal threshold in each region. Contrast variation on the texture can affect the response of the parameters used for the segmentation. This paper shows a method of…

Choosing local matching score method for stereo matching based-on polarization imaging

Polarization imaging is a powerful tool to observe hidden information from an observed object. It has significant advantages, such as computational efficiency (it only needs gray scale images) and can be easily applied by adding a polarizer in front of a camera. Many researchers used polarization in various areas of computer vision, such as object recognition, segmentation and so on. However, there is very little research in stereo vision based on polarization. Stereo vision is a well known technique for obtaining depth information from pairs of stereo digital images. One of the main focuses of research in this area is to get accurate stereo correspondences. In our work, we will study and d…

Retinal vasculature segmentation and measurement framework for color fundus and SLO images

Abstract The change in vascular geometry is an indicator of various health issues linked with vision and cardiovascular risk factors. Early detection and diagnosis of these changes can help patients to select an appropriate treatment option when the disease is in its primary phase. Automatic segmentation and quantification of these vessels would decrease the cost and eliminate inconsistency related to manual grading. However, automatic detection of the vessels is challenging in the presence of retinal pathologies and non-uniform illumination, two common occurrences in clinical settings. This paper presents a novel framework to address the issue of retinal blood vessel detection and width me…

Exudate-based diabetic macular edema detection in fundus images using publicly available datasets

International audience; Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publi…

POLARIZATION-BASED CAR DETECTION

International audience; Road scene understanding is a vital task for driving assistance systems. Robust vehicle detection is a precondition for diverse applications particularly for obstacle avoidance and secure navigation. Color images provide limited information about the physical properties of the object. This results in unstable vehicle detection caused mainly from road scene complexity (strong reflexions, noises and radiometric distortions). Instead, polarimetric images, characteristic of the light wave, can robustly describe important physical properties of the object (e.g., the surface geometric structure, material and roughness etc). This modality gives rich physical informations wh…

Patterned wafer segmentation

This paper is an extension of our previous work on the image segmentation of electronic structures on patterned wafers to improve the defect detection process on optical inspection tools. Die-to-die wafer inspection is based upon the comparison of the same area on two neighborhood dies. The dissimilarities between the images are a result of defects in this area of one of the die. The noise level can vary from one structure to the other, within the same image. Therefore, segmentation is needed to create a mask and apply an optimal threshold in each region. Contrast variation on the texture can affect the response of the parameters used for the segmentation. This paper shows a method to antic…

Textureless macula swelling detection with multiple retinal fundus images

Retinal fundus images acquired with nonmydriatic digital fundus cameras are versatile tools for the diagnosis of various retinal diseases. Because of the ease of use of newer camera models and their relatively low cost, these cameras can be employed by operators with limited training for telemedicine or point-of-care (PoC) applications. We propose a novel technique that uses uncalibrated multiple-view fundus images to analyze the swelling of the macula. This innovation enables the detection and quantitative measurement of swollen areas by remote ophthalmologists. This capability is not available with a single image and prone to error with stereo fundus cameras. We also present automatic alg…

A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases

Accurate three-dimensional (3D) cardiac segmentation from late gadolinium enhancement (LGE)-MRI plays a critical role in designing a structure of reference for diagnosing many cardiac pathologies such as ischemia, myocarditis and myocardial infarction. This segmentation is however still a non-trivial task, due to the motion artifacts during acquisition, and heterogeneous intensity distributions. In this study, we develop a fully 3D automated model based on deep neural networks (DNN) for LGE-MRI myocardial pathologies (scar and No-reflow tissues) segmentation in a new expert annotated dataset. Considering that damaged tissue constitutes a small area of the whole LGE-MRI, we concentrated on m…

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.

Gabor filters in industrial inspection: a review. Application to semiconductor industry

This paper focuses on reviewing some recent works of the use of Gabor filters dealing with industrial applications. After a brief recall of Gabor filter basis, the two usual uses of Gabor filters are recalled: filter bank approach and filter design approach. The third part presents recent published works domain by domain. A fourth part exposes our own work with Gabor Filters for defect detection on semiconductor. A short conclusion summarizes the paper.

Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research

The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored. In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, age-related macular…

Texture Discrimination Using Hierarchical Complex Networks

Texture analysis represents one of the main areas in image processing and computer vision. The current article describes how complex networks have been used in order to represent and characterized textures. More speci?cally, networks are derived from the texture images by expressing pixels as network nodes and similarities between pixels as network edges. Then, measurements such as the node degree, strengths and clustering coe?cient are used in order to quantify properties of the connectivity and topology of the analyzed networks. Because such properties are directly related to the structure of the respective texture images, they can be used as features for characterizing and classifying te…

Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods

Arterial spin labeling (ASL) is an emerging MRI technique for non-invasive measurement of cerebral blood flow (CBF). Compared to invasive perfusion imaging modalities, ASL suffers from low sensitivity due to poor signal-to-noise ratio (SNR), susceptibility to motion artifacts and low spatial resolution, all of which limit its reliability. In this work, the effects of various state of the art image processing techniques for addressing these ASL limitations are investigated. A processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, partial volume effect correction, and CBF quantification was developed and assessed. To fur…

An efficient method for subcutaneous veins localization using Near Infrared imaging

In the majority of the medical treatments, intravenous catheterization is the most important phase in which skilled medical practitioners locate the best vein and perform catheterization process for medication or blood sampling. Due to the different physiological characteristics, mainly darker skin tone, scars or dehydrated condition of patients, medical staff stumbles in localization of veins. This paper proposes an enhanced method which intends to overcome the difficulty faced by medical staff in veins localization for intravenous catheterization. Using the Near Infrared imaging and image processing algorithms a novel approach for veins visualization is proposed. In order to have complete…

Statistical atlas based exudate segmentation

Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent reference methods.

3D and multispectral imaging for subcutaneous veins detection.

The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. Experiments to determine the best NIR wavelengths to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm or wrist surface are presented. For illumination our system is composed of a mercury arc lamp coupled to a 10nm band-pass spectrometer. A structured lighting system is also coupled to our multispectral system in order to provide 3D information of the p…

An Electrooculography based Human Machine Interface for wheelchair control

International audience; This paper presents a novel single channel Electrooculography (EOG) based efficient Human–Machine Interface (HMI) for helping the individuals suffering from severe paralysis or motor degenerative diseases to regain mobility. In this study, we propose a robust system that generates control command using only one type of asynchronous eye activity (voluntary eye blink) to navigate the wheelchair without a need of graphical user interface. This work demonstrates a simple but robust and effective multi-level threshold strategy to generate control commands from multiple features associated with the single, double and triple voluntary eye blinks to control predefined action…

Multimodal Polarimetric And Color Fusion For Road Scene Analysis In Adverse Weather Conditions

Design And Characterization Of Automated Color Sensor System

Abstract The paper presents a color sensor system that can process light reflected from a surface and produce a digital output representing the color of the surface. The end-user interface circuit requires only a 3-bit pseudo flash analog-to-digital converter (ADC) in place of the conventional/typical design comprising ADC, digital signal processor and memory. For scalability and compactness, the ADC was designed such that only two comparators were required regardless of the number of color/wavelength to be identified. The complete system design has been implemented in hardware (bread board) and fully characterized. The ADC achieved less than 0.1 LSB for both INL and DNL. The experimental r…

An Auto-Operated Telepresence System for the Nao Humanoid Robot

International audience; This paper presents the development process of an auto-operated telepresence system for the Nao humanoid robot with the main functionality of directing the robot autonomously to an operator-defined target location within a static workspace. The workspace is observed by an array of top-view cameras, which are used to localize the robot by means of a color-based marker detection technique. The system is accessible world-wide to the remote operator through any Internet-capable device via a web-based control interface. The web server responsible for coordinating the communication between system and operator is hosted on a cloud-based infrastructure online. The system was…

Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors

International audience; Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the b…

Normalization of T2W-MRI Prostate Images using Rician a priori

International audience; Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and over…

Facial geometry and speech analysis for depression detection.

Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high sc…

The PolarLITIS Dataset: Road Scenes Under Fog

Road scene analysis is a fundamental task for both autonomous vehicles and ADAS systems. Nowadays, one can find autonomous vehicles that are able to properly detect objects in the scene in good weather conditions; however, some improvements still need to be done when the visibility is altered. People claim that using some non-conventional sensors such as, infra-red or Lidar, combined with classical vision, enhances road scene analysis in optimal weather conditions. In this work, we present the improvements achieved using polarimetric imaging in the complex situation of some adverse weather conditions. This rich modality is known for its ability to describe an object not only by its intensit…

Subcutaneous Veins Depth Estimation Method Using Monte Carlo Simulations

International audience; Subcutaneous veins localization is basic and important step for any intravenous medication administration. Due to different physiological characteristics, mainly darker skin tone, scars or dehydrated condition of patients, medical staff face difficulty in veins localization. Through near infrared imaging technology the veins can be visualized due to high contrast between veins and skin tissue in this modality. Information on the depth of veins is equally important for proper catheterization or venipuncture procedures. Patients have different veins depth due to the different amount of fat present in the subcutaneous layer. The depth of veins from the skin surface cann…

Computer-Aided Detection for Prostate Cancer Detection based on Multi-Parametric Magnetic Resonance Imaging

International audience; Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer…

Polarimetric image augmentation

Robotics applications in urban environments are subject to obstacles that exhibit specular reflections hampering autonomous navigation. On the other hand, these reflections are highly polarized and this extra information can successfully be used to segment the specular areas. In nature, polarized light is obtained by reflection or scattering. Deep Convolutional Neural Networks (DCNNs) have shown excellent segmentation results, but require a significant amount of data to achieve best performances. The lack of data is usually overcomed by using augmentation methods. However, unlike RGB images, polarization images are not only scalar (intensity) images and standard augmentation techniques cann…

Quantitative comparison of motion history image variants for video-based depression assessment

Abstract Depression is the most prevalent mood disorder and a leading cause of disability worldwide. Automated video-based analyses may afford objective measures to support clinical judgments. In the present paper, categorical depression assessment is addressed by proposing a novel variant of the Motion History Image (MHI) which considers Gabor-inhibited filtered data instead of the original image. Classification results obtained with this method on the AVEC’14 dataset are compared to those derived using (a) an earlier MHI variant, the Landmark Motion History Image (LMHI), and (b) the original MHI. The different motion representations were tested in several combinations of appearance-based …

Automatic measurement of wood fiber orientation and knot detection using an optical system based on heating conduction.

In this paper, a new approach to computing the deviation of wood grain is proposed. To do this, the thermal conduction properties of timber are used (higher conduction in the fiber direction). Exciting the surface of the wood with a laser and capturing the thermal conduction using a thermal camera, an ellipse can be observed. Using a method similar to the tracheid effect, it is possible to extract information from this ellipse, such as the slope of grain and the presence of knots. With this method it is therefore possible to extend the mechanical model (assessing the mechanical properties of timber) to take certain singularities into account. Using this approach, the slope of grain can be e…

A Survey on Microaneurysms Detection in Color Fundus Images

Early Detection of Microaneurysms (MA) plays a vital role in preventing the blindness caused by diabetic retinopathy (DR). DR is preventable yet a serious diabetic problem. Treatment at an earlier stage reduces the risk of blindness. Microaneurysm is the first sign of DR found in fundus images while doing screening. Detection of MA is a challenging task mainly because of its size. MA appears as a tiny red spot ranging from 15µm to 60µm size. The most common way to detect the MA from a colour fundus image is by classification/segmentation through machine learning and deep learning approaches. The FROC-based performance evaluation shows that the existing methods can reach only up to 80% of se…

Microaneurysm detection with radon transform-based classification on retina images.

The creation of an automatic diabetic retinopathy screening system using retina cameras is currently receiving considerable interest in the medical imaging community. The detection of microaneurysms is a key element in this effort. In this work, we propose a new microaneurysms segmentation technique based on a novel application of the radon transform, which is able to identify these lesions without any previous knowledge of the retina morphological features and with minimal image preprocessing. The algorithm has been evaluated on the Retinopathy Online Challenge public dataset, and its performance compares with the best current techniques. The performance is particularly good at low false p…

A low power interface circuit design for a CMOS based smart optical sensor

In this paper, a CMOS interface circuit as part of an optical sensor-based microsystem for pH monitoring applications is presented. The proposed circuit is capable of processing a voltage signal produced by the light transducer and generating 8-bit digital outputs representing the color information (i.e. wavelength). A resolution of 8 different colors has been achieved as a proof of concept and can easily be extended to a higher number of colors without a major modification in the architecture. The proposed interface circuit is a mixed-signal solution that consists of analog as well as digital building blocks along with a light transducer. It can be used as a portable and non-intrusive opti…

Increasing power to predict mild cognitive impairment conversion to Alzheimer's disease using hippocampal atrophy rate and statistical shape models

Identifying mild cognitive impairment (MCI) subjects who will convert to clinical Alzheimer's disease (AD) is important for therapeutic decisions, patient counselling and clinical trials. Hippocampal volume and rate of atrophy predict clinical decline at the MCI stage and progression to AD. In this paper, we create p-maps from the differences in the shape of the hippocampus between 60 normal controls and 60 AD subjects using statistical shape models, and generate different regions of interest (ROI) by thresholding the p-maps at different significance levels. We demonstrate increased statistical power to classify 86 MCI converters and 128 MCI stable subjects using the hippocampal atrophy rat…

Automated Characterization of Mouth Activity for Stress and Anxiety Assessment

International audience; Non-verbal information portrayed by human facial expression, apart from emotional cues also encompasses information relevant to psychophysical status. Mouth activities in particular have been found to correlate with signs of several conditions; depressed people smile less, while those in fatigue yawn more. In this paper, we present a semi-automated, robust and efficient algorithm for extracting mouth activity from video recordings based on Eigen-features and template-matching. The algorithm was evaluated for mouth openings and mouth deformations, on a minimum specification dataset of 640x480 resolution and 15 fps. The extracted features were the signals of mouth expa…

Investigation of Adsorption behaviour of Acetone Vapour towards a Surface Plasmon Resonance Sensing Layer using Adsorption Isotherm Models

Abstract Surface plasmon resonance (SPR) sensors are widely explored due their ultra-sensitivity to even a minute alteration of refractive index. Knowledge of adsorption processes could be exploited to explain the performance and interaction mechanism of an SPR sensor. Here in, we report the fitting of the experimental SPR sensing data during the detection of low concentrations of acetone vapour (0.5-5 ppm) using the linearized and non-linearized format of the Langmuir and the Freundlich isotherm models. The sensing layer is made from a ternary composite material of doped polyaniline, reduced graphene oxide and chitosan. The objective is to find the best model, understand the interaction me…

Designing a framework for assisting depression severity assessment from facial image analysis

Depression is one of the most common mental disorders affecting millions of people worldwide. Developing adjunct tools aiding depression assessment is expected to impact overall health outcomes and treatment cost reduction. To this end, platforms designed for automatic and non-invasive depression assessment could help in detecting signs of the disease on a regular basis, without requiring the physical presence of a mental health professional. Despite the different approaches that can be found in the literature, both in terms of methods and algorithms, a fully satisfactory system for the automatic assessment of depression severity has not been presented as yet. This paper describes a propose…

Simulation of skin reflectance images using 3D tissue modeling and multispectral Monte Carlo light propagation.

In this work we propose a method to simulate the expected, i.e. seen by a camera, multispectral reflectance images of a large skin surface area by combining Monte Carlo light propagation model and realistic tissue modeling based on three dimensional data acquisition of human body areas. In particular, we aim to simulate more accurately light transport in biological tissue by taking into account the geometrical topography of the skin surface, the structure and optical properties of the skin layers, and the subcutaneous veins in presence. We describe our computation method in detail and present simulated reflectance images results.

Surface Reconstruction of Transparent Objects by Polarization Imaging

This paper focuses a method to acquire the surface of transparent objects for 3D measurement. The technique relies on the so called ?Shape from Polarization? technique. The principle of this polarization imaging technique is as follows: after being reflected, an unpolarized light becomes partially linearly polarized. The surface normals can be evaluated by analyzing their polarization parameters and by knowing the refractive index of the object to be controlled. Finally, the 3D shape is obtained by integrating the normals field. After an introduction to expose the problematic, section 2 exposes the principles of polarization technique. The third and the last section deal with the polarimetr…

Plasmonic Biosensors for the Detection of Lung Cancer Biomarkers: A Review

Lung cancer is the most common and deadliest cancer type globally. Its early diagnosis can guarantee a five-year survival rate. Unfortunately, application of the available diagnosis methods such as computed tomography, chest radiograph, magnetic resonance imaging (MRI), ultrasound, low-dose CT scan, bone scans, positron emission tomography (PET), and biopsy is hindered due to one or more problems, such as phenotypic properties of tumours that prevent early detection, invasiveness, expensiveness, and time consumption. Detection of lung cancer biomarkers using a biosensor is reported to solve the problems. Among biosensors, optical biosensors attract greater attention due to being ultra-sensi…

P2D: a self-supervised method for depth estimation from polarimetry

Monocular depth estimation is a recurring subject in the field of computer vision. Its ability to describe scenes via a depth map while reducing the constraints related to the formulation of perspective geometry tends to favor its use. However, despite the constant improvement of algorithms, most methods exploit only colorimetric information. Consequently, robustness to events to which the modality is not sensitive to, like specularity or transparency, is neglected. In response to this phenomenon, we propose using polarimetry as an input for a self-supervised monodepth network. Therefore, we propose exploiting polarization cues to encourage accurate reconstruction of scenes. Furthermore, we…

Promotion et Développement d'un Master Erasmus Mundus - L'Exemple du VIBOT

Cet article decrit l’offre de formation a l’internationale proposee au Centre Universitaire Condorcet du Creusot (Universite de Bourgogne) dans le domaine de la vision par ordinateur et de la robotique. Il presente l’organisation particuliere de ces formations et les actions de support mises en place pour en assurer la perennite.

A more distinctive representation for 3D shape descriptors using principal component analysis

Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method by invoking Principal Component Analysis (PCA) as a classifier to improve the scheme of 3D shape classification. To avoid losing or disordering information after extracting features from the mesh, PCA is used instead of the Classical MDS to discriminate-as much as possible-feature points for each…

A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI

Abstract Objectives: In this work, a new deep learning model for relevant myocardial infarction segmentation from Late Gadolinium Enhancement (LGE)-MRI is proposed. Moreover, our novel segmentation method aims to detect microvascular-obstructed regions accurately. Material and methods: We first segment the anatomical structures, i.e., the left ventricular cavity and the myocardium, to achieve a preliminary segmentation. Then, a shape prior based framework that fuses the 3D U-Net architecture with 3D Autoencoder segmentation framework to constrain the segmentation process of pathological tissues is applied. Results: The proposed network reached outstanding myocardial segmentation compared wi…

Design and Characterization of Automated Color Sensors System

International audience; The paper presents a color sensor system that can process light reflected from a surface and produce a digital output representing the color of the surface. The end-user interface circuit requires only a 3-bit pseudo flash analog-to-digital converter (ADC) in place of the conventional/typical design comprising ADC, digital signal processor and memory. For scalability and compactness, the ADC was designed such that only two comparators were required regardless of the number of color/wavelength to be identified. The complete system design has been implemented in hardware (bread board) and fully characterized. The ADC achieved less than 0.1 LSB for both INL and DNL. The…

3D multispectral light propagation model for subcutaneous veins imaging

In this paper, we describe a new 3D light propagation model aimed at understanding the effects of various physiological properties on subcutaneous vein imaging. In particular, we build upon the well known MCML (Monte Carlo Multi Layer) code and present a tissue model that improves upon the current state-of-the-art by: incorporating physiological variation, such as melanin concentration, fat content, and layer thickness; including veins of varying depth and diameter; using curved surfaces from real arm shapes; and modeling the vessel wall interface. We describe our model, present results from the Monte Carlo modeling, and compare these results with those obtained with other Monte Carlo metho…

Classifying DME vs Normal SD-OCT volumes: A review

International audience; This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this comm…

Effect of Motion Artifact on Digital Camera Based Heart Rate Measurement

International audience; Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting p…

Combining near-infrared illuminants to optimize venous imaging

The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. We previously presented work for localizing near-surface veins via near-infrared (NIR) imaging in combination with structured light ranging for surface mapping and robotic guidance. In this paper, we describe experiments to determine the best NIR wavelengths to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm or wrist surface. For illumination, we empl…

Introducing the Concept of Hyperactivity in Multi Agent Systems

International audience; Software Agents are no longer the simple communication gateways for devices to interconnect using one or more networks. With Multi Agent Systems contributing in a wide spectrum of intelligent systems, the Agents are in a more proactive role than just being responsible for passing messages between their respective base systems. Agent Relation Charts and the Hyperactive Transaction Model in general is one of the recent attempts of developing a multi-view design model for Multi Agent Systems. The model has made a clear distinction in the regular and intelligent activities of an agent. Based on these differences, the agents are classified into three main categories named…

Scanning from heating: 3D shape estimation of transparent objects from local surface heating.

Today, with quality becoming increasingly important, each product requires three-dimensional in-line quality control. On the other hand, the 3D reconstruction of transparent objects is a very difficult problem in computer vision due to transparency and specularity of the surface. This paper proposes a new method, called Scanning From Heating (SFH), to determine the surface shape of transparent objects using laser surface heating and thermal imaging. Furthermore, the application to transparent glass is discussed and results on different surface shapes are presented.

Joint Probability of Shape and Image Similarities to Retrieve 2D TRUS-MR Slice Correspondence for Prostate Biopsy

International audience; This paper presents a novel method to identify the 2D axial Magnetic Resonance (MR) slice from a pre-acquired MR prostate volume that closely corresponds to the 2D axial Transrectal Ultrasound (TRUS) slice obtained during prostate biopsy. The method combines both shape and image intensity information. The segmented prostate contours in both the imaging modalities are described by shape-context representations and matched using the Chi-square distance. Normalized mutual information and correlation coefficient between the TRUS and MR slices are computed to find image similarities. Finally, the joint probability values comprising shape and image similarities are used in…

Content based segmentation of patterned wafers

We extend our previous work on the image segmentation of electronic structures on patterned wafers to improve the defect detection process on optical inspection tools. Die-to-die wafer in- spection is based on the comparison of the same area on two neigh- boring dies. The dissimilarities between the images are a result of defects in this area of one of the dies. The noise level can vary from one structure to the other, within the same image. Therefore, seg- mentation is required to create a mask and apply an optimal thresh- old in each region. Contrast variation on the texture can affect the response of the parameters used for the segmentation. We show a method to anticipate these variation…

Surface plasmon assisted thermal coupling of multiple photon energies

A novel optical effect can be observed in a thin gold foil due to the excitation of surface plasmons which permits a form of all-optical modulation at low pulse rates. Modulated excitation of surface plasmons by infrared photons is shown to couple to several beams at visible-photon energies. The coupling is manifested by the observation of the visible photons being pulsed by the action of the infrared pulses, and by the far field diffraction of the visible beams into concentric rings. When each visible beam also excites surface plasmons, then a quadratic dependence of the visible photon power upon the infrared incident power is measured. The decay of surface plasmons is implicated as the pr…

Texture-less Macula Swelling Detection with Multiple Retinal Fundus Images

International audience

Improving light propagation Monte Carlo simulations with accurate 3D modeling of skin tissue

In this paper, we present a 3D light propagation model to simulate multispectral reflectance images of large skin surface areas. In particular, we aim to simulate more accurately the effects of various physiological properties of the skin in the case of subcutaneous vein imaging compared to existing models. Our method combines a Monte Carlo light propagation model, a realistic three-dimensional model of the skin using parametric surfaces and a vision system for data acquisition. We describe our model in detail, present results from the Monte Carlo modeling and compare our results with those obtained with a well established Monte Carlo model and with real skin reflectance images.

Shape from polarization: a method for solving zenithal angle ambiguity

International audience; We report a multispectral based method that permits the evolution of shape from polarization setup applied to 3D shape estimation of transparent objects. The setup is based on a polarization imaging technique which is a recent imaging method based on the analysis of the polarization state of the light in the observed scene. The technique has rapidly evolved with the development of electro-optic components and some polarization cameras are now available on the market. Shape from polarization consists in measuring the azimuthal and zenithal angles characterizing the normal of each point of the observed surface. We focus on the ambiguity in the measurement of the zenith…

Automatic Assessment of Depression Based on Visual Cues: A Systematic Review

International audience; Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics r…

Spatio-Temporal Saliency Detection in Dynamic Scenes using Local Binary Patterns

International audience; Visual saliency detection is an important step in many computer vision applications, since it reduces further processing steps to regions of interest. Saliency detection in still images is a well-studied topic. However, videos scenes contain more information than static images, and this additional temporal information is an important aspect of human perception. Therefore, it is necessary to include motion information in order to obtain spatio-temporal saliency map for a dynamic scene. In this paper, we introduce a new spatio-temporal saliency detection method for dynamic scenes based on dynamic textures computed with local binary patterns. In particular, we extract l…

Acetone Vapor-Sensing Properties of Chitosan-Polyethylene Glycol Using Surface Plasmon Resonance Technique

To non-invasively monitor and screen for diabetes in patients, there is need to detect low concentration of acetone vapor in the range from 1.8 ppm to 5 ppm, which is the concentration range of acetone vapor in diabetic patients. This work presents an investigation for the utilization of chitosan-polyethylene glycol (PEG)-based surface plasmon resonance (SPR) sensor in the detection of trace concentration acetone vapor in the range of breath acetone in diabetic subjects. The structure, morphology, and elemental composition of the chitosan-PEG sensing layer were characterized using FTIR, UV-VIS, FESEM, EDX, AFM, and XPS methods. Response testing was conducted using low concentration of aceto…

Regularization Preserving Localization of Close Edges

International audience; In this letter, we address the problem of the influence of neighbor edges and their effect on the edge delocalization while extracting a neighbor contour by a derivative approach. The properties to be fulfilled by the regularization operators to minimize or suppress this side effect are deduced, and the best detectors are pointed out. The study is carried out in 1-D for discrete signal. We show that among the derivative filters, one of them can correctly detect our model edges without being influenced by a neighboring transition, whatever their separation distance is and their respective amplitude is. A model of contour and close transitions is presented and used through…

EFFICIENT MACHINE LEARNING FRAMEWORK FOR COMPUTER-AIDED DETECTION OF CEREBRAL MICROBLEEDS USING THE RADON TRANSFORM

International audience; Recent developments of susceptibility weighted MR techniques have improved visualization of venous vasculature and underlying pathologies such as cerebral microbleed (CMB). CMBs are small round hypointense lesions on MRI images that are emerging as a potential biomarker for cerebrovascular disease. CMB manual rating has limited reliability, is time-consuming and is prone to errors as small CMBs can be easily missed or mistaken for venous crosssections. This paper presents a computer-aided detection technique that utilizes a novel cascade of random forest classifiers which are trained on robust Radon-based features with an unbalanced sample distribution. The training …

Deep multimodal fusion for semantic image segmentation: A survey

International audience; Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance improvement. These fusion approaches take the benefits of multiple information sources and generate an optimal joint prediction automatically. This paper describes the essential background concepts of deep multimodal fusion and the relevant applications in compute…

Atmospheric Turbulence Effects Removal on Infrared Sequences Degraded by Local Isoplanatism

When observing an object horizontally at a long distance, degradations due to atmospheric turbulence often occur. Different methods have already been tested to get rid of this kind of degradation, especially on infrared sequences. It has been shown that the Wiener filter applied locally on each frame of a sequence allows to obtain good results in terms of edges, while the regularization by the Laplacian operator applied in the same way provides good results in terms of noise removal in uniform areas. In this article, we present hybrid methods which take advantages of both Wiener filter and Laplacian regularization.

Computer-aided detection of cerebral microbleeds in susceptibility-weighted imaging.

Susceptibility-weighted imaging (SWI) is recognized as the preferred MRI technique for visualizing cerebral vasculature and related pathologies such as cerebral microbleeds (CMBs). Manual identification of CMBs is time-consuming, has limited reliability and reproducibility, and is prone to misinterpretation. In this paper, a novel computer-aided microbleed detection technique based on machine learning is presented: First, spherical-like objects (potential CMB candidates) with their corresponding bounding boxes were detected using a novel multi-scale Laplacian of Gaussian technique. A set of robust 3-dimensional Radon- and Hessian-based shape descriptors within each bounding box were then ex…

P4‐266: Decreases in cerebral blood flow are associated with Aβ status in preclinical Alzheimer's disease

Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning

International audience; Object detection in road scenes is necessary to develop both autonomous vehicles and driving assistance systems. Even if deep neural networks for recognition task have shown great performances using conventional images, they fail to detect objects in road scenes in complex acquisition situations. In contrast, polarization images, characterizing the light wave, can robustly describe important physical properties of the object even under poor illumination or strong reflections. This paper shows how non-conventional polarimetric imaging modality overcomes the classical methods for object detection especially in adverse weather conditions. The efficiency of the proposed …

AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET

International audience; Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, two new methods for the detection of exudates are presented. The methods do not require a lesion training set so the need to ground-truth data is avoided with significant time savings and independence from human error. We evaluate our algorithm with a new publicly available dataset from various ethnic groups and levels of DME. Also, we compare our results with two recent exudate segmentation algorithms on the same dataset. In all of …

3D reconstruction of transparent objects exploiting surface fluorescence caused by UV irradiation

In this paper, we present a novel approach exploiting fluorescence imaging to estimate the shape of transparent objects. Classical inspection systems require users to coat transparent objects with some powder before measurement. Methods suggested in literature through non contact measurement do not effectively deal with the refraction problem, thus, providing inaccuracies. The proposed method handles the scanning of transparent objects without using any powder and solving the refraction problem using UV environment. A classical triangulation method based on stereovision scheme using fixed stereoscopic visible range cameras with a fixed UV (Ultra Violet) laser source is implemented. Transpar…

Steerable wavelet transform for atlas based retinal lesion segmentation

International audience; Computer aided diagnosis and follow up can help in prevention and treatment of diabetes and its related complications. Screening of diabetes related disease in the eyes is done by a special low cost fundus camera. A follow up of the patients visiting at di fferent time intervals for screening brings us to the problem of image analysis for change detection and its cost per patient. It is very likely that human annotations for the lesions may be erroneous and often time consuming. Since the ethnic background plays a signi cant role in retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in patients and retinal images, an eth…

Near-infrared imaging and structured light ranging for automatic catheter insertion

Vein localization and catheter insertion constitute the first and perhaps most important phase of many medical procedures. Currently, catheterization is performed manually by trained personnel. This process can prove problematic, however, depending upon various physiological factors of the patient. We present in this paper initial work for localizing surface veins via near-infrared (NIR) imaging and structured light ranging. The eventual goal of the system is to serve as the guidance for a fully automatic (i.e., robotic) catheterization device. Our proposed system is based upon near-infrared (NIR) imaging, which has previously been shown effective in enhancing the visibility of surface vein…

Robotics Studies in Europe

This paper describes the organization and teaching methodologies for mechanics and robotics related subjects in the European Erasmus Mundus master programmes EMARO (European master in Advanced Robotics), and VIBOT (master courses in VIsion & roBOTics). The structure of these masters is overviewed, the experience in designing and managing them is outlined and we point out the common effort for a global reach of these programmes and to build a transnational teaching architecture.

A deep learning approach for the segmentation of myocardial diseases

Cardiac left ventricular (LV) segmentation is a paramount essential step for both diagnosis and treatment of cardiac pathologies such as ischemia, myocardial infarction, arrhythmia and myocarditis. However, this segmentation is challenging due to high variability across patients and the potential lack of contrast between structures. In this work, we propose and evaluate a (2.5D) SegU-Net model based on the fusion of two deep learning segmentation techniques (U-Net and Seg-Net) for automated LGE-MRI (Late gadolinium enhanced magnetic resonance imaging) myocardial disease (infarct core and no-reflow region) quantification in a new multifield expert annotated dataset. Given that the scar tissu…

Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

International audience; Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only…

Observation of Knudsen effect with microcantilevers

The Knudsen effect is estimated theoretically and observed experimentally using a U-shaped silicon microcantilever. Though Knudsen forces are extremely small in most cases involving microcantilevers, there exist situations where these forces can be significant and may be important in atomic force microscopy and in microelectromechanical systems (MEMS). The criteria for the presence of Knudsen forces are outlined and an analytical expression in the form of a linear function of the pressure is given for the force in the free molecular regime. The experimental results display peaks in the transitional regime while varying linearly in the molecular regime.

Détection de la dépression par l’analyse de la géométrie faciale et de la parole

Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high sc…

Noise Robustness Analysis of Point Cloud Descriptors

In this paper, we investigate the effect of noise on 3D point cloud descriptors. Various types of point cloud descriptors have been introduced in the recent years due to advances in computing power, which makes processing point cloud data more feasible. Most of these descriptors describe the orientation difference between pairs of 3D points in the object and represent these differences in a histogram. Earlier studies dealt with the performances of different point cloud descriptors; however, no study has ever discussed the effect of noise on the descriptors performances. This paper presents a comparison of performance for nine different local and global descriptors amidst 10 varying levels o…

Efficient 3D Deep Learning for Myocardial Diseases Segmentation

Automated myocardial segmentation from late gadolinium enhancement magnetic resonance images (LGE-MRI) is a critical step in the diagnosis of cardiac pathologies such as ischemia and myocardial infarction. This paper proposes a deep learning framework for improved myocardial diseases segmentation. In the first step, we build an encoder-decoder segmentation network that generates myocardium and cavity segmentations from the whole volume, followed by a 3D U-Net based on Shape prior to identifying myocardial infarction and myocardium ventricular obstruction (MVO) segmentations from the encoder-decoder prediction. The proposed network achieves good segmentation performance, as computed by avera…

Automated detection of microaneurysms using robust blob descriptors

International audience; Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fun…

Classification of SD-OCT Volumes with LBP: Application to DME Detection

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Our method is based on Local Binary Patterns (LBP) features to describe the texture of Optical Coherence Tomography (OCT) images and we compare different LBP features extraction approaches to compute a single signature for the whole OCT volume. Experimental results with two datasets of respectively 32 and 30 OCT volumes show that regardless of using low or high level representations, features derived from LBP texture have highly discriminative power. Moreover, the experimen…

MULTISPECTRAL VENOUS IMAGES ANALYSIS FOR OPTIMUM ILLUMINATION SELECTION

International audience; Intravenous (IV) catheterization is the most important phase in medical practices of daily life. It is hard to localize veins in patients who have deep veins, minor age or dark skin; hence multiple attempts become indispensable for proper catheterization in such cases. Near Infrared (NIR) Imaging allow to visualize the veins underneath the skin of persons having non-visibility of veins problem. This paper reports the pre-selection of illuminants that ensure best veins/tissues contrast for patients having different skin tone. The sample subjects have been divided in four different classes based on the Luminance value of their skin tone in order to extract the best ill…

AUTOMATIC DETECTION OF SMALL SPHERICAL LESIONS USING MULTISCALE APPROACH IN 3D MEDICAL IMAGES

International audience; Automated detection of small, low level shapes such as circular/spherical objects in images is a challenging computer vision problem. For many applications, especially microbleed detection in Alzheimer's disease, an automatic pre-screening scheme is required to identify potential seeds with high sensitivity and reasonable specificity. A new method is proposed to detect spherical objects in 3D medical images within the multi-scale Laplacian of Gaussian framework. The major contributions are (1) breaking down 3D sphere detection into 1D line profile detection along each coordinate dimension, (2) identifying center of structures by normalizing the line response profile …

Lesion Segmentation in Breast Sonography

Sonography is gaining popularity as an adjunct screening technique for assessing abnormalities in the breast This is particularly true in cases where the subject has dense breast tissue, wherein widespread techniques like Digital Mammography (DM) fail to produce reliable outcomes This article proposes a novel and fully automatic methodology for breast lesion segmentation in B-mode Ultra-Sound (US) images by utilizing region, boundary and shape information to cope up with the inherent artifacts present in US images The proposed approach has been evaluated using a set of sonographic images with accompanying expert-provided ground truth.

Bag of words representation and SVM classifier for timber knots detection on color images

Knots as well as their density have a huge impact on the mechanical properties of wood boards. This paper addresses the issue of their automatic detection. An image processing pipeline which associates low level processing (contrast enhancement, thresholding, mathematical morphology) with bag-of-words approach is developed. We propose a SVM classification based on features obtained by SURF descriptors on RGB images, followed by a dictionary created using the bag-of-words approach. Our method was tested on color images from two different datasets with a total number of 640 knots. The mean recall (true positive) rate achieved was (92%) and (97%) for a single dictionary (built only on samples …