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White Matter Lesions (WMLs) are small areas of dead cells found in parts of the brain. In general, it is difficult for medical experts to accurately quantify the WMLs due to decreased contrast between White Matter (WM) and Grey Matter (GM). The aim of this paper is to automatically detect the White Matter Lesions which is present in the brains of elderly people. WML detection process includes the following stages: 1. Image preprocessing, 2. Clustering (Fuzzy c-means clustering (FCM), Geostatistical Possibilistic clustering (GPC) and Geostatistical Fuzzy clustering (GFCM)). The proposed system is tested on a database of 208 MRI images. GFCM yields high sensitivity of 90%, specificity of 94% and overall accuracy of 95% over FCM and GPC. The detection results reveal that GFCM better localizes the large regions of lesions and gives less false positive rate when compared to FCM and GPC which captures the largest loads of WMLs only in the upper ventral horns of the brain.

Automated Detection of White Matter Lesions in MRI Brain Images Using Spatiofuzzy and Spatio-Possibilistic Clustering Models