Registration and analysis for images couple : application to mammograms

In this thesis, the problem addressed is the development of a computer-aided diagnosis system (CAD) based on conjoint analysis of several images, and therefore on the comparison of these medical images. The particularity of our approach is to look for evolutions or aberrant new tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of tissues. This problem allows to apprehend one aspect of the analysis of a medical file performed by experts which is the study of a case through comparison and evolution detection. The methodology proposed is carried out within the application context of the development of a CAD applied to mammograms. The first step when a couple of images are involved is to perform an adapted registration. Any automated comparison of signals requires an alignment of similar components present on the pictures, that is to say a registration phase, so that they occupy the same space on the two images. As the registration is never perfect, we must take into account the level of uncertainty and develop a comparison method able to distinguish registration error and real small differences between comparable tissues. In many applications, the assessment of similarity used during the registration step is also used in the interpretation step that yields to prompt suspicious regions. In our case, registration is assumed to match the spatial coordinates of similar anatomical elements

Segmentation du muscle pectoral sur une mammographie

National audienceL'objectif de cette étude est la segmentation du muscle pectoral sur une mammographie. Ce travail a pour but de faciliter le recalage géométrique de paires d'images mammographiques par l'extraction fiable de points de repère. La sémantique de l'image est exploitée pour extraire des points de repère basés sur l'anatomie. La technique d'extraction utilisée est basée sur les contours actifs pour lesquels nous proposons de construire différentes énergies

Visual perception driven registration of mammograms

International audienceThis paper aims to develop a methodology to register pairs of temporal mammograms. Control points based on anatomical features are detected in an automated way. Thereby, image semantic is used to extract landmarks based on these control points. A referential is generated from these control points based on this referential the studied images are realigned using different levels of observation leading to both rigid and non-rigid transforms according to expert mammogram reading

Recalage et analyse d'un couple d'images (application aux mammographies)

Dans le monde de la recherche, l analyse du signal et plus particulièrement d image, est un domaine très actif, de par la variété des applications existantes, avec des problématiques telles que la compression de données, la vidéo-surveillance ou encore l analyse d images médicales pour ne prendre que quelques exemples. Le mémoire s inscrit dans ce dernier domaine particulièrement actif. Le nombre d appareils d acquisition existant ainsi que le nombre de clichés réalisés, entraînent la production d une masse importante d informations à traiter par les praticiens. Ces derniers peuvent aujourd hui être assistés par l outil informatique. Dans cette thèse, l objectif est l élaboration d un système d aide au diagnostic, fondé sur l analyse conjointe, et donc la comparaison d images médicales. Notre approche permet de détecter des évolutions, ou des tissus aberrants dans un ensemble donné, plutôt que de tenter de caractériser, avec un très fort a priori, le type de tissu cherché.Cette problématique permet d appréhender un aspect de l analyse du dossier médical d un patient effectuée par les experts qui est l étude d un dossier à travers le suivi des évolutions. Cette tâche n est pas aisée à automatiser. L œil humain effectue quasi-automatiquement des traitements qu il faut reproduire. Avant de comparer des régions présentes sur deux images, il faut déterminer où se situent ces zones dans les clichés. Toute comparaison automatisée de signaux nécessite une phase de recalage, un alignement des composantes présentes sur les clichés afin qu elles occupent la même position sur les deux images. Cette opération ne permet pas, dans le cadre d images médicales, d obtenir un alignement parfait des tissus en tous points, elle ne peut que minimiser les écarts entre tissus. La projection d une réalité 3D sur une image 2D entraîne des différences liées à l orientation de la prise de vue, et ne permet pas d analyser une paire de clichés par une simple différence entre images. Différentes structurations des clichés ainsi que différents champs de déformation sont ici élaborés afin de recaler les images de manière efficace.Après avoir minimisé les différences entre les positions sur les clichés, l analyse de l évolution des tissus n est pas menée au niveau des pixels, mais à celui des tissus eux-mêmes, comme le ferait un praticien. Afin de traiter les clichés en suivant cette logique, les images numériques sont réinterprétées, non plus en pixels de différentes luminosités, mais en motifs représentatifs de l ensemble de l image, permettant une nouvelle décomposition des clichés, une décomposition parcimonieuse. L atout d une telle représentation est qu elle permet de mettre en lumière un autre aspect du signal, et d analyser sous un angle nouveau, les informations nécessaires à l aide au diagnostic.Cette thèse a été effectuée au sein du laboratoire LIPADE de l Université Paris Descartes (équipe SIP, spécialisée en analyse d images) en collaboration avec la Société Fenics (concepteur de stations d aide au diagnostic pour l analyse de mammographies) dans le cadre d un contrat Cifre.In the scientific world, signal analysis and especially image analysis is a very active area, due to the variety of existing applications, with issues such as file compression, video surveillance or medical image analysis. This last area is particularly active. The number of existing devices and the number of pictures taken, cause the production of a large amount of information to be processed by practitioners. They can now be assisted by computers.In this thesis, the problem addressed is the development of a computer diagnostic aided system based on conjoint analysis, and therefore on the comparison of medical images. This approach allows to look for evolutions or aberrant tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of fabric sought.This problem allows to apprehend an aspect of the analysis of medical file performed by experts which is the study of a case through the comparison of evolutions.This task is not easy to automate. The human eye performs quasi-automatically treatments that we need to replicate.Before comparing some region on the two images, we need to determine where this area is located on both pictures. Any automated comparison of signals requires a registration phase, an alignment of components present on the pictures, so that they occupy the same space on the two images. Although the characteristics of the processed images allow the development of a smart registration, the projection of a 3D reality onto a 2D image causes differences due to the orientation of the tissues observed, and will not allow to analyze a pair of shots with a simple difference between images. Different structuring of the pictures and different deformation fields are developed here to efficiently address the registration problem.After having minimized the differences on the pictures, the analysis of tissues evolution is not performed at pixels level, but the tissues themselves, as will an expert. To process the images in this logic, they will be reinterpreted, not as pixels of different brightness, but as patterns representative of the entire image, enabling a new decomposition of the pictures. The advantage of such a representation is that it allows to highlight another aspect of the signal, and analyze under a new perspective the information necessary to the diagnosis aid.This thesis has been carried out in the LIPADE laboratory of University Paris Descartes (SIP team, specialized in image analysis) and in collaboration with the Society Fenics (designer of diagnosis aid stations in the analysis of mammograms) under a Cifre convention. The convergence of the research fields of those teams led to the development of this document.PARIS5-Bibliotheque electronique (751069902) / SudocSudocFranceF

A Code Tagging Approach to Software Product Line Development:An Application to Satellite Communication Libraries

International audienceSoftware product line engineering seeks to systematise reuse when developing families of similar software systems so as to minimise development time, cost and defects. To realise variability at the code level, product line methods classically advocate usage of inheritance, components, frameworks, aspects or generative techniques. However, these might require unaffordable paradigm shifts for developers if the software was not thought at the outset as a product line. Furthermore, these techniques can be conflicting with a company's coding practices or external regulations. These concerns were the motivation for the industry- university collaboration described in this paper in which we developed a minimally intrusive coding technique based on tags. The approach was complemented with traceability from code to feature diagrams which were exploited for automated configuration. It is supported by a toolchain and is now in use in the partner company for the development of flight grade satellite communica- tion software libraries

A Formal Semantics for Feature Cardinalities in Feature Diagrams

Feature cardinalities in feature diagrams determine the num-ber of times a feature and its subtree can be duplicated dur-ing configuration by an operation named “cloning”. Other authors already investigated the problem and pub-lished different proposals of semantics for this construct. However, this previous work is not easily amenable to the formal study of the various properties of feature diagrams and their derived configurations. Also, cross-tree constraint languages still need to be properly extended to account for feature cardinalities. This paper presents an extension of an earlier formal se-mantics of feature diagrams by adding support for feature cardinalities. 1