Michel Eboueya

Cooperation of fuzzy segmentation operators for correction aliasing phenomenon in 3D color Doppler imaging.

The study described in this paper concerns natural object modeling in the context of uncertain, imprecise and inconsistent representation. We propose a fuzzy system which offers a global modeling of object properties such as color, shape, velocity, etc. This modeling makes a transition from a low level reasoning (pixel level), which implies a local precise but uncertain representation, to a high level reasoning (region level), inducing a certain assignment. So, we use fuzzy structured partitions characterizing these properties. At this level. each property will have its own global modeling. Then, these different models are merged for decision making. Our approach was tested with several applications. In particular, we show here its performance in the area of blood flow analysis from 3D color Doppler images in order to quantify and study the development of this flow. We present methods that detect and correct aliasing phenomenon, i.e. inconsistent information. At first, the flow space is partitioned into fuzzy sectors where each sector is defined by a center, an angle and a direction. In parallel, the velocity information carried by the pixels is classified into fuzzy classes. Then, by combining these two partitions, we obtain the velocity distribution into sectors. Moreover, for each found path (from the first sector to the last one), we locate and correct inconsistent velocities by applying global rules. After extracting some meaningful sector features, the fuzzy modeling, applied to the aliasing correction, makes it possible to simplify and synthesize the blood flow direction.

Describing and Researching of Learning Resources with Ontology Model

In this paper, we introduce to describe learning resources by using ontology model based on metadata of learning standard as sharable content object reference model (SCORM) and Dublin Core metadata. Then we present how to research and discover them with query language as SPARQL query language. The designing of metadata structure has followed the standard metadata and we have added some categories and elements to managing the indexation and classification. To find resources in our ontology we show some examples of retrieving them by using SPARQL query language. For our study we have proved them by using an ontology tool as Protege package

Localisation et détection de stents sur des images rayons-X bas contraste

Resume Actuellement, de plus en plus d’endoprotheses vasculaires, ou stents, sont implantes pour traiter les stenoses. La qualite du deploiement du stent dans les vaisseaux est supposee etre un important facteur de restenose. Le probleme quotidien rencontre en routine clinique est donc la verification de l’expansion du stent. Pour verifier de maniere non invasive et rapide cette derniere, l’utilisation des images rayons X est preferable, mais les stents possedent un tres faible contraste sur de telles images. Nous proposons de les localiser et de les detecter automatiquement en appliquant sur des images radios, des methodes adaptees a la detection de pliure. Dans cet article, nous comparons dans un premier temps, deux methodes classiques de segmentation, l’une d’elle est fondee sur la valeur des niveaux de gris, et l’autre fondee sur un filtrage adapte. La comparaison est faite sur le taux de fausses detections, les methodes ayant ete testees sur une centaine d’images. Dans un second temps, nous proposons un detecteur multilocal flou afin de detecter de facon plus precise les mailles du stent.

DOCINER: A Document Indexation Tool for Learning Objects

In this paper, we present a method we implemented to help a user index documents (and, in particular, learning objects) according to a given set of concepts (terms referring to domains or topics). The user first associates keywords to the concepts. Our method uses such associations to suggest simple rules for indexing a document by concepts according to the keywords this document contains. Then, our system uses those rules to perform the indexation of documents.

Stent detection for presentation by overlay in injected x-ray cardiac images

Coronary stenting is now a widely - used technique for the treatment of stenosis of coronary arteries. One of the main difficulties for the interventionists is the difficulty of locating the dilated stent in the image and controlling its accurate positioning with respect to the lesion. Indeed, the stent is less contrasted than injected vessels and can hardly be located in the injected frames. We propose to help them by identifying the stent in the non-injected frames and then showing it by overlay in the injected frames. Physicians can then more easily appreciate the relative positioning of the stent and the pathology. For the detection task, the performance of our algorithm varies around 80% with a standard deviation about 10%. This result has been obtained with more than 20 series. Each of them includes about 20 non-injected frames acquired on a LC+ system. At least one stent is present in these series. Our database includes stents whose radio opacity varies in a large range. With our non optimized implementation, time computation for a 512*512 image is about 20 seconds. This work is a first step in the use of image processing techniques for the segmentation of the prothesis that are routinely used by interventionists. By locating them in the image, the x-ray imaging system will be able to provide a better display of them.