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

Authors:
Hassan Khotanlou;Olivier Colliot;Jamal Atif;Isabelle Bloch
Affiliations:
TELECOM ParisTech (ENST), CNRS UMR 5141 LTCI, IFR 49, Paris, France;CNRS, UPR 640 - Cognitive Neuroscience and Brain Imaging Laboratory, Université Pierre et Marie Curie - Paris 6, Hôpital de la Pitié-Salpêtrière, IFR 49, Paris, France;Groupe de Recherche sur les Energies Renouvelables (GRER), Université des Antilles et de la Guyane, Campus de St Denis, 97 300 Cayenne, France;TELECOM ParisTech (ENST), CNRS UMR 5141 LTCI, IFR 49, Paris, France
Venue:
Fuzzy Sets and Systems
Year:
2009

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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.