This paper presents an automatic method for the segmentation of Glioblastoma multiforme(GBM) tumors from MRI images. The global search ability of Genetic Algorithm (GA) to optimize the Fuzzy C-means (FCM) clustering algorithm to obtain better clustering center. But the prematurity problem of GA itself has bad effects on the whole clustering. Therefore, in order to optimize the traditional GA-FCM algorithm�s clustering effect, in this work, we introduce the Opposition-based learning mechanism into GA, to construct an OBL-Genetic Algorithm (OBL-GA). The improved algorithm forms the next generation of evolutionary population by selecting the superior individuals in the collection of the sub generation and reverse sub generation, to increase the population diversity, and final to overcome the prematurity problem of GA. Then applying the improved algorithm to FCM, which gives better results and then resultant image, is applied with level sets, to exact delineation of GBM tumor. The validation is performed on a labeled BRATS data set. Our segmentation results are highly accurate, and compare favorably to the state of the art.

Fuzzy-c means,Glioblastoma multiforme,Segmentation,Genetic Algorithm,Opposition based learning,MRI

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