3D brain tumor segmentation using fuzzy classification and deformable models

Authors:
Hassan Khotanlou;Jamal Atif;Olivier Colliot;Isabelle Bloch
Affiliations:
Dept TSI, CNRS UMR 5141, GET – Ecole Nationale Supérieure des Télécommunications, Paris, France;Dept TSI, CNRS UMR 5141, GET – Ecole Nationale Supérieure des Télécommunications, Paris, France;McConnell Brain Imaging Center, MNI, McGill University, Montréal, Québec, Canada;Dept TSI, CNRS UMR 5141, GET – Ecole Nationale Supérieure des Télécommunications, Paris, France
Venue:
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Year:
2005

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Abstract

A new method that automatically detects and segments brain tumors in 3D MR images is presented. An initial detection is performed by a fuzzy possibilistic clustering technique and morphological operations, while a deformable model is used to achieve a precise segmentation. This method has been successfully applied on five 3D images with tumors of different sizes and different locations, showing that the combination of region-based and contour-based methods improves the segmentation of brain tumors.