Credal Fusion of Classifications for Noisy and Uncertain Data

This paper reports on an investigation in classification technique employed to classify noised and uncertain data. However, classification is not an easy task. It is a significant challenge to discover knowledge from uncertain data. In fact, we can find many problems. More time we don't have a good or a big learning database for supervised classification. Also, when training data contains noise or missing values, classification accuracy will be affected dramatically. So to extract groups from  data is not easy to do. They are overlapped and not very separated from each other. Another problem which can be cited here is the uncertainty due to measuring devices. Consequentially classification model is not so robust and strong to classify new objects. In this work, we present a novel classification algorithm to cover these problems. We materialize our main idea by using belief function theory to do combination between classification and clustering. This theory treats very well imprecision and uncertainty linked to classification. Experimental results show that our approach has ability to significantly improve the quality of classification of generic database

Segmentation et quantification volumique des thrombosesveineuses (application aux images échographiques)

La thrombose veineuse est la principale cause de l'embolie pulmonaire. Cette maladie est fatale si elle n'est pas détectée et soignée à temps. Les approches manuelles utilisées par les spécialistes ne sont pas assez précises pour détecter ces complications. Notre travail concerne la segmentation des thromboses veineuses et la mesure de leurs volumes, afin de fournir aux praticiens un système d'aide au diagnostic robuste et précis. En effet, si plusieurs modalités d'imagerie peuvent être utilisées pour diagnostiquer la thrombose veineuse l'échographie se présente comme l'examen le plus adéquat. Afin d'atteindre nos objectifs, nous proposons un algorithme robuste pour la détection des contours des thromboses veineuses dans des images échographiques 2D. En premier lieu, nous adoptons la diffusion anisotrope pour un pré-traitement de ce type d'image connue par son faible contenu d'information, afin de réduire le bruit appelé speckle. Nous proposons d'utiliser les contours actifs précédés d'une initialisation semi-automatique basée sur un modèle elliptique. Cette initialisation repose sur la génération d'une ellipse à partir de cinq points déterminés au préalable par l'expert. En effet, ce choix est dû à la forme pseudo-elliptique des thromboses veineuses. Une fois les contours obtenus, une modélisation géométrique est nécessaire, pour déterminer leurs surfaces et leurs volumes. Pour ceci, nous proposons une triangulation de Delaunay adaptative suivie d'une projection des normales principales des surfaces triangulées. Cette méthodologie est intégrée dans un système d'acquisition et de positionnement des images 2D dans un espace 3D fixe. Ce positionnement est issu d'une procédure de calibration basée sur l'adoption d'un localisateur électromagnétique. Une première validation avec deux spécialistes sur des fantômes cylindrique en plastic et sur des thrombus in vitro et in vivo a eu lieu. Les résultats obtenus montrent une précision élevée pour la quantification volumique, permettant d'envisager son utilisation en routine clinique, offrant ainsi une assistance fiable aux praticiens.RENNES1-BU Sciences Philo (352382102) / SudocBREST-Télécom Bretagne (290192306) / SudocSudocFranceF

Chimney gas detection in seismic imaging using morphology and optical flow

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Suivi du positionnement d'une sonde à l'aide d'un capteur électromagnétique tridimensionnel

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Validation of Calibration by Spatial Registration Between US and MRI Scans: Application to Bifurcation of Carotid

International audienceIn this paper, we present a new system for interactively calibration algorithm validate by registering three-dimensional US and three-dimensional magnetic resonance imaging (MRI) to explore atheromatous lesions in carotid. This registration becomes to validate calibration process of US probe, which is based on alignment of US and MR image. This calibration is based on electromagnetic system called PCBird to provide a free-hand system. An extensive analysis of the errors of our system was performed by using a custom-built plastic cylinder phantom. Error calibration was found to have a mean value of 1.93 mm. The registration error between US and MR space was dependent on the distance of the target point from the US probe face

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

THE CONRIBUTION OF FUSION TECHNIQUES IN THE RECOGNITION SYSTEMS OF RADAR TARGETS

International audienceFor several years, different types of classifiers have been developed using several features vector in the automatic target recognition (ATR) field. However, because of measurement conditions and extraction techniques, the individual results performance obtained by different approaches of classification are varied and different. For these reasons and as part of the radar target recognition, we present in this paper a study which deals with a comparison of four fusion techniques : voting majority, Bayesian fusion, belief fusion and possibility fusion. To implement these fusion methods, we used three classifiers: the Support Vector Machines SVM, Neurons Networks and K Nearest Neighbor (KNN) to improve the final decision for ATR. In addition,we present the results obtained from real data performed in the anechoic chamber of ENSTA Bretagne. Thereby demonstrating the contribution, performance and robustness of the approach developed and applied in aid to recognition of radar target

Computer assisted venous thrombosis quantification

International audienceVenous thrombosis (VT) volume assessment, by verifying its risk of progression when anticoagulant or thrombolytic therapies are prescribed, is often necessary to screen life- threatening complications. Commonly, VT volume estimation is done by manual delineation of few contours in the ultrasound (US) image sequence, assuming that the VT has a regular shape and constant radius, thus producing significant errors. This paper presents and evaluates a comprehensive functional approach based on the combination of robust anisotropic diffusion and deformable contours to calculate VT volume in a more accurate manner when applied to freehand 2-D US image sequences. Robust anisotropic filtering reduces image speckle noise without generating incoherent edge discontinuities. Prior knowledge of the VT shape allows initializing the deformable contour, which is then guided by the noise-filtering outcome. Segmented contours are subsequently used to calculate VT volume. The proposed approach is integrated into a system prototype compatible with existing clinical US machines that additionally tracks the acquired images 3-D position and provides a dense Delaunay triangulation required for volume calculation. A predefined robust anisotropic diffusion and deformable contour parameter set enhances the system usability. Experimental results pertinence is assessed by comparison with manual and tetrahedron-based volume computations, using images acquired by two medical experts of eight plastic phantoms and eight in vitro VTs, whose independently measured volume is the reference ground truth. Results show a mean difference between 16 and 35 mm3 for volumes that vary from 655 to 2826 mm3. Two in vivo VT volumes are also calculated to illustrate how this approach could be applied in clinical conditions when the real value is unknown. Comparative results for the two experts differ from 1.2% to 10.08% of the smallest estimated value when the image acquisition cadences are similar

Volume calculation of venous thrombosis using 2D ultrasound images

International audienceVenous thrombosis screening exams use 2D ultrasound images, from which medical experts obtain a rough idea of the thrombosis aspect and infer an approximate volume. Such estimation is essential to follow up the thrombosis evolution. This paper proposes a method to calculate venous thrombosis volume from non-parallel 2D ultrasound images, taking advantage of a priori knowledge about the thrombosis shape. An interactive ellipse fitting contour segmentation extracts the 2D thrombosis contours. Then, a Delaunay triangulation is applied to the set of 2D segmented contours positioned in 3D, and the area that each contour defines, to obtain a global thrombosis 3D surface reconstruction, with a dense triangulation inside the contours. Volume is calculated from the obtained surface and contours triangulation, using a maximum unit normal component approach. Preliminary results obtained on 3 plastic phantoms and 3 in vitro venous thromboses, as well as one in vivo case are presented and discussed. An error rate of volume estimation inferior to 4,5% for the plastic phantoms, and 3,5% for the in vitro venous thromboses was obtained

Détection des Contours des Thrombus Veineux dans les Images Echographiques

International audienceDétection des Contours des Thrombus Veineux dans les Images Echographique