Fuzzy spatial growing for glioblastoma multiforme segmentation on brain magnetic resonance imaging

Abstract

Image segmentation is a fundamental technique in medical applications. For example, the extraction of biometrical parameter of tumors is of paramount importance both for clinical practice and for clinical studies that evaluate new brain tumor therapies. Tumor segmentation from brain Magnetic Resonance Images (MRI) is a difficult task due to strong signal heterogeneities and weak contrast at the boundary delimitation. In this work we propose a new framework to segment the Glioblastoma Multiforme (GBM) from brain MRI. The proposed algorithm was constructed based on two well known techniques: Region Growing and Fuzzy C-Means. Furthermore, it considers the intricate nature of the GBM in MRI and incorporates a fuzzy formulation of Region Growing with an automatic initialization of the seed points. We report the performance results of our segmentation framework on brain MRI obtained from patients of the chilean Carlos Van Buren Hospital and we compare the results with Region Growing and the classic Fuzzy C-Means approaches. © Springer-Verlag Berlin Heidelberg 2007.