A Hybrid Technique Based on Combining Fuzzy K-means Clustering and Region Growing for Improving Gray Matter and White Matter Segmentation

In this paper we present a hybrid approach based on combining fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms to measure gray (GM) and white matter (WM) tissue. The proposed algorithm uses intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different clusters using fuzzy k-means clustering. The centers of these clusters are the input to the region growing (SRG) method for creating the closed regions. The outputs of SRG technique are fed to sensitivity and specificity algorithm to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results. KeywordsFuzzy clustering; seed region growing; performance measure; MRI brain database; sensitivity and specificity.