IMPROVED TISSUE SEGMENTATION TECHNIQUE FOR BRAIN MRI

Abstract

The aim of this study was to minimize the execution cost and maximize the noise-robustness of automatic segmentation of brain Magnetic Resonance Images (MRI). Partitioning brain tissues in MRI is considerably tough as they present low contrast and intensity inhomogeneity. The case is further aggravated due to inherent noise from imaging environment. Conventionally, Fuzzy CMeans (FCM) clustering is employed for tissue classification; however, this technique is costly and sensitive to noise. Moreover, FCM can best detect spherical clusters only due to which this technique becomes less accurate for brain images. Therefore, Gustafson-Kessel (G-K) clustering technique is employed which has the capability to detect clusters of dissimilar shapes and sizes. Nonetheless, G-K is also expensive and sensitive to noise. This technique has yet not achieved a satisfactory accuracy level either, mainly because of high sensitivity to initialization. Hence, a few modifications are presented in an effort to produce an efficient and robust G-K algorithm for tissue segmentation. First, a histogram-based method is proposed for G-K initialization. Second, cluster volume estimates are inputted to the algorithm for utilizing its ability of detecting variable sized clusters. Third, intensity information is fused with textural information in G-K objective function for better classification. The algorithm is used to segment test MRI of brain into three classes: Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). Experimental results show that the proposed algorithm performs better than conventional FCM and GK in terms of both execution cost and robustness.