Brain magnetic resonance image segmentation using novel improvement for expectation maximizing.

OBJECTIVE To improve the quality of expectation maximizing (EM) for brain image segmentation, and to evaluate the accuracy of segmentation results. METHODS This brain segmentation study was conducted in Universiti Putra Malaysia in Serdong, Malaysia between February and November 2010 on simulated and real images using novel improvement for EM. The EM-1 (proposed algorithm) was compared with neighborhood based extensions for fuzzy C-mean (FCM). The EM-1 was also applied to all 20 normal real MRI volumes and compared with reported results from the Internet Brain Segmentation Repository. RESULTS In simulated images, the EM-1 outperforms neighborhood based extensions for FCM. The average similarity index value of the proposed algorithm for all 20 normal images is 0.802. The EM-1 produces the average Jaccard indices ρ higher than other algorithms and near to manual results. The average similarity indices ρ for EM-1 and FCM extensions (FCM with spatial information [FCM-S], Fast Generalized FCM [FGFCM]) for all 20 normal images are: EM-1=0.802, FCM-S=0.7517, enhanced FCM=0.7581, and FGFCM=0.7597. CONCLUSION Experimental results show that the proposed algorithm performs better than other studied algorithms on various noise levels in terms of similarity index, ρ.