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Intégration de connaissances anatomiques a priori dans des modèles géométriques

Abstract : Medical imaging is a principal data source for different applications. Even though medical images represent a lot of knowledge concerning the studied case, all the a priori knowledge known by the specialist remains implicit. Nevertheless this a priori knowledge has a major role in the interpretation and the use of the images. In this thesis, anatomical a priori knowledge is integrated in two medical applications. First, an automatic processing pipeline is proposed in order to detect, quantify and localize aneurysms on a segmented cerebrovascular tree. Centerlines of blood vessels are extracted and then used to automatically detect aneurysms and quantify them. To localize aneurysm, a matching is made between the cerebrovascular tree of the patient and a healthy one. The a priori knowledge, in this case, is represented by a graph. In the context of identifying sub-parts of an organ represented by a mesh, we propose the use of an anatomical ontology. This ontology is first enhanced by all information necessary to achieve the task of mesh segmenting. A new algorithm using this ontology to accomplish the segmentation task is then proposed.
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Submitted on : Monday, February 6, 2012 - 11:54:47 AM
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  • HAL Id : tel-00607260, version 2



Sahar Hassan. Intégration de connaissances anatomiques a priori dans des modèles géométriques. Mathématiques générales [math.GM]. Université de Grenoble, 2011. Français. ⟨NNT : 2011GRENM020⟩. ⟨tel-00607260v2⟩



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