Search results for "Image Segmentation"

Feature extraction and correlation for time-to-impact segmentation using log-polar images

2004

In this article we present a technique that allows high-speed movement analysis using the accurate displacement measurement given by the feature extraction and correlation method. Specially, we demonstrate that it is possible to use the time to impact computation for object segmentation. This segmentation allows the detection of objects at different distances.

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

2006

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…

Classification of Melanoma Lesions Using Sparse Coded Features and Random Forests

2016

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…

Toward morphological thoracic EIT: major signal sources correspond to respective organ locations in CT.

2012

Lung and cardiovascular monitoring applications of electrical impedance tomography (EIT) require localization of relevant functional structures or organs of interest within the reconstructed images. We describe an algorithm for automatic detection of heart and lung regions in a time series of EIT images. Using EIT reconstruction based on anatomical models, candidate regions are identified in the frequency domain and image-based classification techniques applied. The algorithm was validated on a set of simultaneously recorded EIT and CT data in pigs. In all cases, identified regions in EIT images corresponded to those manually segmented in the matched CT image. Results demonstrate the abilit…

Mean sets for building 3D probabilistic liver atlas from perfusion MR images

2012

This paper is concerned with liver atlas construction. One of the most important issues in the framework of computational abdominal anatomy is to define an atlas that provides a priori information for common medical task such as registration and segmentation. Unlike other approaches already proposed so far (to our knowledge), in this paper we propose to use the concept of random compact mean set to build probabilistic liver atlases. To accomplish this task a two-tier process was carried out. First a set of 3D images was manually segmented by a physician. We see the different 3D segmented shapes as a realization of a random compact set. Secondly, elements of two known definitions of mean set…

Change-driven Image Architecture on FPGA with adaptive threshold for Optical-Flow Computation

2006

Optical flow computation has been extensively used for object motion estimation in image sequences. However, the results obtained by most optical flow techniques are as accurate as computationally intensive due to the large amount of data involved. A new strategy for image sequence processing has been developed; pixels of the image sequence that significantly change fire the execution of the operations related to the image processing algorithm. The data reduction achieved with this strategy allows a significant optical flow computation speed-up. Furthermore, FPGAs allow the implementation of a custom data-flow architecture specially suited for this strategy. The foundations of the change-dr…

3D contour based local manual correction of tumor segmentations in CT scans

2009

Segmentation is an essential task in medical image analysis. For example measuring tumor growth in consecutive CT scans based on the volume of the tumor requires a good segmentation. Since manual segmentation takes too much time in clinical routine automatic segmentation algorithms are typically used. However there are always cases where an automatic segmentation fails to provide an acceptable segmentation for example due to low contrast, noise or structures of the same density lying close to the lesion. These erroneous segmentation masks need to be manually corrected. We present a novel method for fast three-dimensional local manual correction of segmentation masks. The user needs to draw …

Efficient correspondence problem-solving in 3-D shape reconstruction using a structured light system

2005

This paper deals with 3-D object reconstruction using a structured light system (SLS). The SLS is composed of a camera and a laser projector that illuminates spots on the scene of interest. The basic problem of such a system is the correspondence problem. If the correct correspondence between the imaged spots and the projected laser rays is found, the 3-D coordinates of the physical points associated with these spots can be calculated. We propose a method that automatically provides SLS configurations (i.e., the relative positions of both camera and laser projector with respect to the object to be analyzed) that allow performing an unambiguous and direct correspondence procedure. Experiment…

2020

Abstract Background and objective Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches. Methods We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitudina…

Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods

2006

We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitP…