Download or read book Segmentation par contours actifs en imagerie m dicale dynamique written by Éric Debreuve and published by . This book was released on 2000 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: En imagerie d'émission, la médecine nucléaire fournit une information fonctionnelle sur l'organe étudié. En imagerie de transmission, elle fournit une information anatomique, destinée par exemple à corriger certains facteurs de dégradation des images d'émission. Qu'il s'agisse d'une image d'émission ou de transmission, il est utile de savoir extraire de façon automatique ou semi-automatique les éléments pertinents : le ou les organes d'intérêt et le pourtour du patient lorsque le champ d'acquisition est large. Voilà le but des méthodes de segmentation. Nous avons développé deux méthodes de segmentation par contours actifs, le point crucial étant la définition de leur vitesse d'évolution. Elles ont été mises en oeuvre par les ensembles de niveaux. En premier lieu, nous nous sommes intéressés à l'imagerie statique de transmission de la cage thoracique. La vitesse d'évolution, définie heuristiquement, fait directement intervenir les projections acquises. La carte de transmission segmentée, obtenue ainsi sans reconstruction, doit servir à améliorer la correction de l'atténuation photonique subie par les images cardiaques d'émission. Puis nous avons étudié la segmentation des séquences cardiaques -d'émission- synchronisées par électrocardiogramme. La méthode de segmentation spatio-temporelle développée résulte de la minimisation d'un critère variationnel exploitant d'un bloc l'ensemble de la séquence. La segmentation obtenue doit servir au calcul de paramètres physiologiques. Nous l'avons illustré en calculant la fraction d'éjection. Pour terminer, nous avons exploité les propriétés des ensembles de niveaux afin de développer une méthode géométrique de recalage, non rigide et non paramétrique. Nous l'avons appliquée à la compensation cinétique des images des séquences cardiaques synchronisées. (...)

Download or read book Les contours actifs une m thode de s gmentation written by Jean-Jacques Rousselle and published by . This book was released on 2003 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les méthodes de segmentation d'images sont nombreuses ; toutes présentent des avantages mais ne donnent pas entière satisfaction. Toutes doivent être adaptées en fonction des appplications que l'on se propose de réaliser. Les contours actifs ou modèles déformables ont permis de s'affranchir du chaînage des points du contour mais nécessitent le réglage de nombreux paramètres. Les contours actifs que nous avons étudiés sont implémentés par un algorithme "greedy". D'abord, nous proposons une variante basée sur une minimisation par algorithme génétique. Puis nous présentons trois approches pour régler les paramètres qui contrôlent l'évolution du contour. Les plans d'expériences permettent, sur un jeu d'images, de choisir très rapidement un jeu de paramètres performants. Les algorithmes génétiques peuvent être utilisés pour optimiser les paramètres. Enfin, nous décrivons une approche originale où les paramètres sont locaux et tirés aléatoirement. Ces contours actifs autonomes permettent uné évolution des contours sans aucun réglage. Les applications développés trouvent leur intérêt dans le domaine médical.

Download or read book Active Contour Based Segmentation Techniques for Medical Image Analysis written by R.J. Hemalatha and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Image processing is a technique which is used to derive information from the images. Segmentation is a section of image processing for the separation or segregation of information from the required target region of the image. There are different techniques used for segmentation of pixels of interest from the image. Active contour is one of the active models in segmentation techniques, which makes use of the energy constraints and forces in the image for separation of region of interest. Active contour defines a separate boundary or curvature for the regions of target object for segmentation. The contour depends on various constraints based on which they are classified into different types such as gradient vector flow, balloon and geometric models. Active contour models are used in various image processing applications specifically in medical image processing. In medical imaging, active contours are used in segmentation of regions from different medical images such as brain CT images, MRI images of different organs, cardiac images and different images of regions in the human body. Active contours can also be used in motion tracking and stereo tracking. Thus, the active contour segmentation is used for the separation of pixels of interest for different image processing.

Download or read book Robust Image Segmentation using Active Contours Level Set Approaches written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. A partial solution to the problem of internal edges is to partition an image based on the statistical information of image intensity measured within sub-regions instead of looking for edges. Although representing an image as a piecewise-constant or unimodal probability density functions produces better results than traditional edge-based methods, the performances of such methods is still poor on images with sub-regions consisting of multiple components, e.g. a zebra on the field. The segmentation of this kind of multispectral images is even a more difficult problem. The object of this work is to develop advanced segmentation methods which provide robust performance on the images with non-uniform sub-regions. In this work, we propose a framework for image segmentation which partitions an image based on the statistics of image intensity where the statistical information is represented as a mixture of probability density functions defined in a multi-dimensional image intensity space. Depending on the method to estimate the mixture density functions, three active contour models are proposed: unsupervised multi-dimensional histogram method, half-supervised multivariate Gaussian mixture density method, and supervised multivariate Gaussian mixture density method. The implementation of active cont.

Download or read book Active Contour Based Segmentation in Medical Imaging written by Chunming Li and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Active contours have been extensively used in image processing and computer vision. The existing active contour models can be broadly classified as either parametric active contour or snake models or geometric active contour. In this research, we investigate some fundamental and important issues in these two types of active contours and apply our methods to different modalities of images, with emphasis on medical images. For parametric active contours, we propose an improved gradient vector flow (GVF) as the external force, which has a desirable edge-preserving property. We call this vector field edge preserving gradient vector flow (EPGVF). In snake models, automatic initialization and topological changes are difficult problems. We solve these problems by segmentation of external force field. The segmented force field is then used for automatic initialization and splitting of snakes. To segment the external force field, we represent it with a graph, and a graph-theory approach can be taken to determine the membership of each pixel. In traditional geometric active contour models, the level set function has to be periodically re-initialized during the evolution, in order to maintain stable evolution and usable results. The re-initialization procedure has serious problems, such as when and how to re-initialize, and there is no answer that generally applies to date. Moreover, the re-initialization is computationally expensive and can cause errors in computation. We present a new level set method that completely eliminates the need of re-initialization. Our level set evolution is derived from an energy functional minimization problem. The energy functional consists of an internal energy term that penalizes the deviation of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set. Compared with traditional level set methods, our method has several advantages, such as faster convergence, more accurate computation, and more efficient and flexible initialization. Moreover, our level set formulation can be easily implemented with an efficient and stable narrow band level set evolution algorithm. Our level set method can be extended to 3D and higher dimension.

Download or read book Image Segmentation with Variational Active Contours written by Xavier Bresson and published by . This book was released on 2005 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Medical Image Segmentation Using Edge based Active Contours written by Alaa Khadidos and published by . This book was released on 2016 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi resolution Image Segmentation Using Geometric Active Contours written by Po-Yan Tsang and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi resolution Image Segmentation Using Geometirc Active Contours written by Po-Yan Tsang and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation is an important step in image processing, with many applications such as pattern recognition, object detection, and medical image analysis. It is a technique that separates objects of interests from the background in an image. Geometric active contour is a recent image segmentation method that overcomes previous problems with snakes. It is an attractive method for medical image segmentation as it is able to capture the object of interest in one continuous curve. The theory and implementation details of geometric active contours are discussed in this work. The robustness of the algorithm is tested through a series of tests, involving both synthetic images and medical images. Curve leaking past boundaries is a common problem in cases of non-ideal edges. Noise is also problematic for the advancement of the curve. Smoothing and parameters selection are discussed as ways to help solve these problems. This work also explores the incorporation of the multi-resolution method of Gaussian pyramids into the algorithm. Multi-resolution methods, used extensively in the areas of denoising and edge-selection, can help capture the spatial structure of an image. Results show that similar to the multi-resolution methods applied to parametric active contours, the multi-resolution can greatly increase the computation without sacrificing performance. In fact, results show that with successive smoothing and sub-sampling, performance often improves. Although smoothing and parameter adjustment help improve the performance of geometric active contours, the edge-based approach is still localized and the improvement is limited. Region-based approaches are recommended for further work on active contours.

Download or read book La d tection de contours actifs dynamiques dans l imagerie m dicale written by Chokri Ferkous and published by . This book was released on 2019-06-28 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book SUBDIVIDE AND CONQUER written by Anaïs Laure Marie-Thérèse Badoual and published by . This book was released on 2019 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: Biomedical image analysis ; segmentation ; active contours/surfaces ; parametrization ; local refinement ; subdivision ; multiresolution ; texture ; splines ; usable software.

Download or read book Image Segmentation written by Pei-Gee Ho and published by IntechOpen. This book was released on 2011-04-19 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: It was estimated that 80% of the information received by human is visual. Image processing is evolving fast and continually. During the past 10 years, there has been a significant research increase in image segmentation. To study a specific object in an image, its boundary can be highlighted by an image segmentation procedure. The objective of the image segmentation is to simplify the representation of pictures into meaningful information by partitioning into image regions. Image segmentation is a technique to locate certain objects or boundaries within an image. There are many algorithms and techniques have been developed to solve image segmentation problems, the research topics in this book such as level set, active contour, AR time series image modeling, Support Vector Machines, Pixon based image segmentations, region similarity metric based technique, statistical ANN and JSEG algorithm were written in details. This book brings together many different aspects of the current research on several fields associated to digital image segmentation. Four parts allowed gathering the 27 chapters around the following topics: Survey of Image Segmentation Algorithms, Image Segmentation methods, Image Segmentation Applications and Hardware Implementation. The readers will find the contents in this book enjoyable and get many helpful ideas and overviews on their own study.

Download or read book Active Contours in Image Segmentation written by Tadeusz Pawlowski and published by . This book was released on 1995 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi resolution Active Models for Image Segmentation written by Ahmed Gawish and published by . This book was released on 2015 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation refers to the process of subdividing an image into a set of non-overlapping regions. Image segmentation is a critical and essential step to almost all higher level image processing and pattern recognition approaches, where a good segmentation relieves higher level applications from considering irrelevant and noise data in the image. Image segmentation is also considered as the most challenging image processing step due to several reasons including spatial discontinuity of the region of interest and the absence of a universally accepted criteria for image segmentation. Among the huge number of segmentation approaches, active contour models or simply snakes receive a great attention in the literature. Where the contour/boundary of the region of interest is defined as the set of pixels at which the active contour reaches its equilibrium state. In general, two forces control the movement of the snake inside the image, internal force that prevents the snake from stretching and bending and external force that pulls the snake towards the desired object boundaries. One main limitation of active contour models is their sensitivity to image noise. Specifically, noise sensitivity leads the active contour to fail to properly converge, getting caught on spurious image features, preventing the iterative solver from taking large steps towards the final contour. Additionally, active contour initialization forms another type of limitation. Where, especially in noisy images, the active contour needs to be initialized relatively close to the object of interest, otherwise the active contour will be pulled by other non-real/spurious image features. This dissertation, aiming to improve the active model-based segmentation, introduces two models for building up the external force of the active contour. The first model builds up a scale-based-weighted gradient map from all resolutions of the undecimated wavelet transform, with preference given to coarse gradients over fine gradients. The undecimated wavelet transform, due to its near shift-invariance and the absence of down-sampling properties, produces well-localized gradient maps at all resolutions of the transform. Hence, the proposed final weighted gradient map is able to better drive the snake towards its final equilibrium state. Unlike other multiscale active contour algorithms that define a snake at each level of the hierarchy, our model defines a single snake with the external force field is simultaneously built based on gradient maps from all scales. The second model proposes the incorporation of the directional information, revealed by the dual tree complex wavelet transform (DT CWT), into the external force field of the active contour. At each resolution of the transform, a steerable set of convolution kernels is created and used for external force generation. In the proposed model, the size and the orientation of the kernels depend on the scale of the DT CWT and the local orientation statistics of each pixel. Experimental results using nature, synthetic and Optical Coherent Tomography (OCT) images reflect the superiority of the proposed models over the classical and the state-of-the-art models.

Download or read book Robust Image Segmentation Using Active Contours written by Cheolha Pedro Lee and published by . This book was released on 2005 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: level sets, active contours, segmentation.

Download or read book Object Segmentation Combining Cage Active Contours and Active Appearance Models written by Albert Busqué Plaza and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a novel method for image segmentation based on the combination of Cage Active Contours (CAC) and the well-known Active Appearance Models (AAM) methods. The proposed method is applied to segment some structures from MRI images of the brain.

Download or read book Contours actifs param triques pour la segmentation d images et vid os written by Frédéric Precioso and published by . This book was released on 2004 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cette thèse s’inscrit dans le cadre des modèles de contours actifs. Il s’agit de méthodes dynamiques appliquées à la segmentation d’image, en image fixe et vidéo. L’image est représentée par des descripteurs régions et/ou contours. La segmentation est traitée comme un problème de minimisation d’une fonctionnelle. La recherche du minimum se fait via la propagation d’un contour actif di basé régions. L’efficacité de ces méthodes réside surtout dans leur robustesse et leur rapidité. L’objectif de cette thèse est triple : le développement (i) d’une représentation paramétrique de courbes respectant certaines contraintes de régularités, (ii) les conditions nécessaires à une évolution stable de ces courbes et (iii) la réduction des coûts de calcul afin de proposer une méthode adaptée aux applications nécessitant une réponse en temps réel. Nous nous intéressons principalement aux contraintes de rigidité autorisant une plus grande robustesse vis-à-vis du bruit. Concernant l’évolution des contours actifs, nous étudions les problèmes d’application de la force de propagation, de la gestion de la topologie et des conditions de convergence. Nous avons fait le choix des courbes splines cubiques. Cette famille de courbes offre d’intéressantes propriétés de régularité, autorise le calcul exact des grandeurs différentielles qui interviennent dans la fonctionnelle et réduit considérablement le volume de données à traiter. En outre, nous avons étendu le modèle classique des splines d’interpolation à un modèle de splines d’approximation, dites smoothin splines. Ce dernier met en balance la contrainte de régularité et l’erreur d’interpolation sur les points d’échantillonnage du contour. Cette flexibilité permet ainsi de privilégier la précision ou la robustesse. L’implémentation de ces modèles de splines a prouvé son efficacité dans diverses applications de segmentation.