Stéphane Chemouny

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In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a(More)
In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification(More)
Knowledge of the anatomy of the forefoot is important for understanding its mechanical pathology and developing specific surgical procedures. The aim of this study was to quantify 3-dimensional morphological parameters, which were proposed for the characterization of the metatarsal intrinsic anatomy. Thirty-five metatarsal bones prepared from 7 cadaver(More)
Low-grade gliomas (WHO grade II) are diffusively infiltrative brain tumors arising from glial cells. Spatial classification that is usually based on cerebral lobes lacks accuracy and is far from being able to provide some pattern or statistical interpretation of their appearance. In this paper, we propose a novel approach to understand and infer position of(More)
Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas(More)
In this paper we propose a novel approach for detection, segmentation and characterization of brain tumors. Our method exploits prior knowledge in the form of a sparse graph representing the expected spatial positions of tumor classes. Such information is coupled with imagebased classification techniques along with spatial smoothness constraints towards(More)
In this paper we propose a novel approach for detection, segmentation and characterization of brain tumors. Our method exploits prior knowledge in the form of a sparse graph representing the expected spatial positions of tumor classes. Such information is coupled with image-based classification techniques along with spatial smoothness constraints towards(More)
Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the discrete nature of the quantized search space. In this paper(More)
Carpal morphology and orientation of carpal bones are usually studied on two-plane radiography. Those measurements depend on the incidence of X-ray and on the expertise of physician. A method that eliminates both should improve the accuracy of those measurements. The digital data from computed tomography scans can be use to describe carpal geometry. We(More)