0000000000136593
AUTHOR
Robert Martí
showing 23 related works from this author
A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance
2011
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…
Automatic Seed Placement for Breast Lesion Segmentation on US Images
2012
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…
A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images
2013
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…
A Survey of Prostate Segmentation Methodologies in Ultrasound, Magnetic Resonance and Computed Tomography Images
2012
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
2015
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…
Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation
2011
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…
Graph Cut Energy Minimization in a Probabilistic Learning Framework for 3D Prostate Segmentation in MRI
2012
International audience; Variations in inter-patient prostate shape, and size and imaging artifacts in magnetic resonance images (MRI) hinders automatic accurate prostate segmentation. In this paper we propose a graph cut based energy minimization of the posterior probabilities obtained in a supervised learning schema for automatic 3D segmentation of the prostate in MRI. A probabilistic classification of the prostate voxels is achieved with a probabilistic atlas and a random forest based learning framework. The posterior probabilities are combined to obtain the likelihood of a voxel being prostate. Finally, 3D graph cut based energy minimization in the stochastic space provides segmentation …
Weighted Likelihood Function of Multiple Statistical Parameters to Retrieve 2D TRUS-MR Slice Correspondece for Prostate Biopsy
2012
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…
Spectral clustering of shape and probability prior models for automatic prostate segmentation.
2013
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…
Texture Guided Active Appearance Model Propagation for Prostate Segmentation
2010
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…
A Shape-based Statistical Method to Retrieve 2D TRUS-MR Slice Correspondence for Prostate Biopsy
2012
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.
2012
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…
A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images
2012
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
2012
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…
SIFT Texture Description for Understanding Breast Ultrasound Images
2014
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.
A hybrid framework of multiple active appearance models and global registration for 3D prostate segmentation in MRI.
2012
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
2015
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 …
Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation
2011
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…
A Non-linear Diffeomorphic Framework for Prostate Multimodal Registration
2011
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…
Normalization of T2W-MRI Prostate Images using Rician a priori
2016
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…
Computer-Aided Detection for Prostate Cancer Detection based on Multi-Parametric Magnetic Resonance Imaging
2017
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…
Joint Probability of Shape and Image Similarities to Retrieve 2D TRUS-MR Slice Correspondence for Prostate Biopsy
2012
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…
Lesion Segmentation in Breast Sonography
2010
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.