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3D brain tumors and internal brain structures segmentation in MR images

Abstract : The main topic of this thesis is to segment brain tumors, their components (edema and necrosis) and internal structures of the brain in 3D MR images. For tumor segmentation we propose a framework that is a combination of region-based and boundary-based paradigms. In this framework, we first segment the brain using a method adapted for pathological cases and extract some global information on the tumor by symmetrybased histogram analysis. The second step segments the tumor and its components. For this, we propose a new and original method that combines region and boundary information in two phases: initialization and refinement. For initialization, which is mostly region-based, we present two new methods. The first one is a new fuzzy classification method which combines the membership, typicality and neighborhood information of the voxels. The second one relies on symmetry-based histogram analysis. The initial segmentation of the tumor is refined relying on boundary information of the image. This method is a deformable model constrained by spatial relations. The spatial relations are obtained based on the initial segmentation and surrounded tissues of the tumor. The proposed method can be used for a large class of tumors in any modality of MR images. To segment a tumor and its components full automatically the proposed framework needs only a contrast enhanced T1-weighted image and a FLAIR image. In the case of a contrast enhanced T1-weighted image only, some user interaction will be needed. We evaluated this method on a data set of 20 contrast enhanced T1-weighted and 10 FLAIR images with different types of tumors. Another aim of this thesis is the segmentation of internal brain structures in the presence of a tumor. For this, a priori knowledge about the anatomy and the spatial organization of the structures is provided by an ontology. To segment each structure, we first exploit its relative spatial position from a priori knowledge. We then select the spatial relations which remain consistent using the information on the segmented tumor. These spatial relations are then fuzzified and fused in a framework proposed by our group. As for the tumor, the segmentation process of each structure has two steps. In the first step we search the initial segmentation of the structure in a globally segmented brain. The search process is done in the region of interest (ROI) provided by the fused spatial relations. To globally segment the brain structures we use two methods, the first one is the proposed fuzzy classification and the second one is a multiphase level sets. To refine the initial segmentation, we use a deformable model which is again constrained by the fused spatial relations of the structure. This method was also evaluated on 10 contrast enhanced T1-weighted images to segment the ventricles, caudate nucleus and thalamus.
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https://pastel.archives-ouvertes.fr/pastel-00003662
Contributor : Ecole Télécom Paristech <>
Submitted on : Friday, January 9, 2009 - 8:00:00 AM
Last modification on : Friday, July 31, 2020 - 10:44:05 AM
Long-term archiving on: : Wednesday, September 8, 2010 - 6:06:07 PM

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Hassan Khotanlou. 3D brain tumors and internal brain structures segmentation in MR images. domain_other. Télécom ParisTech, 2008. English. ⟨pastel-00003662⟩

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