3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models

Abstract : We propose a new general method for segmenting brain tumors in 3D magnetic resonance images. Our method is applicable to different types of tumors. First, the brain is segmented using a new approach, robust to the presence of tumors. Then a first tumor detection is performed, based on selecting asymmetric areas with respect to the approximate brain symmetry plane and fuzzy classification. Its result constitutes the initialization of a segmentation method based on a combination of a deformable model and spatial relations, leading to a precise segmentation of the tumors. Imprecision and variability are taken into account at all levels, using appropriate fuzzy models. The results obtained on different types of tumors have been evaluated by comparison with manual segmentations.
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Hassan Khotanlou, Olivier Colliot, Jamal Atif, Isabelle Bloch. 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets and Systems, Elsevier, 2009, 160 (10), ⟨10.1016/j.fss.2008.11.016⟩. ⟨hal-01251278⟩

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