Fusion de l'information : Fusion de données et de modèles appliqués à la segmentation d'images écho-endoscopiques

· De nos jours, la fusion de l'information est un sujet en pleine effervescence. Les applications de segmentation d'images médicales, auxquelles nous nous intéressons, nécessitent la plupart du temps d'effectuer la fusion de différentes sources de connaissances. Après une présentation générale de la fusion de l'information et des architectures correspondantes, cet article expose une méthode de détection de la paroi interne de l'oesophage à partir d'images écho-endoscopiques, basée sur la fusion de données et de modèles. La fusion de données repose sur la logique floue de telle sorte que le moteur de traitement peut être perçu comme un système monocapteur / multisource de fusion. La fusion de modèles est réalisée grâce à une utilisation des contours actifs adaptée aux spécificités des images traitées et à l'architecture du système de traitement choisie afin d'aboutir à une décision binaire, c'est-à-dire à la détection de la paroi oesophagienne

Analyse d'images échographiques de loesophage, reconstruction 3D et interprétation

Ce travail concerne l'évaluation des approches orientées système à base de connaissances pour l'interprétation des images médicales, en application à l'échoendoscopie oesophagienne et le développement d'un système d'aide au staging des tumeurs. Il est montré comment les approches intelligentes (systèmes experts et fusion d'information) peuvent permettre de rationaliser l'utilisation de l'ensemble de connaissances à priori. L'extraction pertinente de structures anatomiques, dans notre cas, la structure oesophagienne, devient une application naturelle de l'ingénierie des connaissances. Cette extraction s'appuie sur une segmentation des images. La robustesse requise pour ces algorithmes impose le développement d'architectures avancées de traitement permettant de compenser le faible contenu numérique de ces images. Trois exemples concrets sont détaillés : l'extraction 2D de l'interface oesophagienne interne, l'extraction 3D des interfaces oesophagiennes et , le suivi spatial avec la reconstruction 3D de l'artère aorte. Les connaissances sont représentées par des modèles statiques ou dynamiques (modèles flous, géométriques ou évidentiels). Nous avons examiné une approche qui exploite la complémentarité des probabilités et de la logique floue pour obtenir une représentation fidèle des connaissances à priori. Modèles flous et réalité statistique sont mis en adéquation dans une base d'apprentissage. Il est montré comment toutes ces composantes peuvent être intégrées dans une architecture cohérente et hiérarchiquement organisée.This work concerns the approach evaluations, which are oriented knowledge based system for medical images interpretation applied to esophagus echoendoscopy and the development of aid system for tumor staging. It's shown how the intelligent approaches (expert system and information fusion) can allow rationalizing the using of a priori knowledge. The pertinent extraction of anatomic structures, in our case, esophagus structure, becomes a natural application in the knowledge engineering. This extraction is based on the image segmentation. The required robustness for these algorithms impose the advanced architectures development allowing the compensation of low numerical content of these images. Three concrete examples are detailed : 2D extraction of the esophagus' interface, 3D extraction of the esophagus' interfaces and spatial following with 3D reconstruction of the aorta. Knowledge is represented by static or dynamical model (fuzzy, geometric or evidential models). The approach using the complementarities of probabilities and fuzzy logic to obtain presentation exact of knowledge a priori. Fuzzy models and statistic reality are synchronized by a knowledge base. It's shown that all components can be integrated in a coherent architecture hierarchically organized.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Interprétation d'images médicales échographiques - Représentation des connaissances et fusion d'information pour l'extraction des structures anatomiques pertinentes

International audienceInterprétation d'images médicales échographiques - Représentation des connaissances et fusion d'information pour l'extraction des structures anatomiques pertinente

Utilisation de méthodes basées sur la théorie des ensembles flous pour l'extraction de contours

International audienceUtilisation de méthodes basées sur la théorie des ensembles flous pour l'extraction de contour

Calibration d'une sonde échographique : application pour la quantification volumique des thromboses

International audienceCalibration d'une sonde échographique : application pour la quantification volumique des thrombose

Probabilistic and fuzzy information fusion applied to radar system ranking

International audienceThe decision making systems make use of heterogeneous information to identify an object class or a target, which are affected by various kinds of imperfection. First, information issued from measures (radar measures, images) of an observation is represented by X variables. Generally, on these X variables, each class can be described through a probability distribution function. These decision systems also integrate expert a prior knowledge to assist the decision. Such information is defined by Y variables and is represented by fuzzy membership function. The question is how to combine appropriately these two kinds of data in order to improve the decision process. In this paper, we present a decision model combining probabilistic and fuzzy data. The decision is defined using a fuzzy Bayesian approach, which takes into account these two imperfections. Only two classes are considered using one X variable and one Y variable. Then an extension is proposed to more complicated cases. To validate the interest of this approach, we compare it with the Bayesian classification and fuzzy classification applied separately to synthetic data. In addition, we will see how our approach can be applied to the problem of radar system ranking, on which system resources are limited and as a consequence, decisions about priorities must be taken. Using the system information sources (i.e. probabilistic: radar measurements, fuzzy: prior expert knowledge, evidential), a comparison between Bayesian classification, fuzzy classification, system decision and the proposed approach is presented

Data fusion and stochastic optimization : application to esophagus outer wall detection on ultrasound images

International audienceWe propose a detection method of esophagus outer wall from endosonographic sequences (composed of separate slices uniformly distributed), which minimizes the information alterations due to the cooperation of different models. The kernel of the proposed solution is based on the use of a stochastic optimization algorithm, fully adapted to our particular case: the goal is to find the optimal contour, which verifies regularity conditions and maximizes a given criteria. Such a method presents the advantage of taking into consideration the entire searched space and thus, avoiding local minimum optimization problems. Moreover, this approach cooperates with a data fusion based processing, which allows a priori knowledge integration with its own inaccuracy. Detection robustness is finally maintained by the use of control agents, which take advantage of adjacent slices. At this level, fuzzy fusion and evidence theory are confronted. All these components are integrated in a coherent and cooperative architecture. Results obtained on real images acquired are very encouraging

Fuzzy fusion and belief updating. Application to esophagus wall detection on ultra sound images

International audienceIn medical ultrasound imaging, information is corrupted by inaccuracy (due to data, acquisition modality, noise), uncertainty (due to noise and missing data) and ambiguity (several anatomical structures having the same ultrasound respond). In this work, we propose a 3D segmentation method of esophagus inner and outer wall from endosonographic sequences (composed of separate slices uniformly distributed), which minimizes these information alterations thanks to the cooperation of different models. The proposed solution is based on the use of a stochastic optimization algorithm, fully adapted to our particular case. The goal is to find the optimal surface, which verifies regularity conditions and maximizes a given criteria. Moreover, this approach cooperates with a data fusion based processing, which allows a prior knowledge integration with its own inaccuracy. All these components are integrated in a coherent architecture hierarchically organized which allows belief updating. First results obtained on real images acquired in a medical center are presented

Le Projet Télé-Médecine en GastroEntérologie : une approche multimédia au dossier patient

International audienceLe Projet Télé-Médecine en GastroEntérologie : une approche multimédia au dossier patien

Toward determination of venous thrombosis ages by using fuzzy logic and supervised Bayes classification

International audienceVenous thrombosis is a common pathology that creates serious problems in public health. The diagnosis of thromboses, particularly the determination of their relative ages can be efficiently accomplished by ultrasound imaging. This study intends to classify automatically the thrombosis ages by using a predefined learning base that depends on a prior knowledge of physicians. In practice, this learning base is affected by information imperfections of the type ambiguity since physicians cannot give exact thrombosis ages. Thus, the proposed learning base is constructed in a 3-tuple: observation, label, membership value in term of fuzzy logic for each class and not a 2-tuple as in the usual supervised Bayes classification application. By considering this "fuzzy learning base", a method modeling simultaneously the concept of probabilistic uncertainty and ambiguity is proposed. In this approach, the probability for a given observation is considered on the membership value of each class and not on the class itself. At this level, the discussion focuses on two types of applications: the thrombosis age classification and the definition of membership function by using a fuzzy learning base for classification