Graph-based Detection, Segmentation & Characterization of Brain Tumors

Abstract : 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 producing a reliable detection map within a unified graphical model formulation. Towards optimal use of prior knowledge, a two layer interconnected graph is considered with one layer corresponding to the low-grade glioma type (characterization) and the second layer to voxel-based decisions of tumor presence. Efficient linear programming both in terms of performance as well as in terms of computational load is considered to recover the lowest potential of the objective function. The outcome of the method refers to both tumor segmentation as well as their characterization. Promising results on substantial data sets demonstrate the extreme potentials of our method.
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Communication dans un congrès
CVPR - 25th IEEE Conference on Computer Vision and Pattern Recognition 2012, Jun 2012, Providence, United States. 2012
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https://hal.inria.fr/hal-00712714
Contributeur : Sarah Parisot <>
Soumis le : mercredi 27 juin 2012 - 20:19:08
Dernière modification le : vendredi 6 avril 2018 - 13:32:01
Document(s) archivé(s) le : vendredi 28 septembre 2012 - 02:45:58

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  • HAL Id : hal-00712714, version 1

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Sarah Parisot, Hugues Duffau, Stéphane Chemouny, Nikos Paragios. Graph-based Detection, Segmentation & Characterization of Brain Tumors. CVPR - 25th IEEE Conference on Computer Vision and Pattern Recognition 2012, Jun 2012, Providence, United States. 2012. 〈hal-00712714〉

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