A Review for Segmentation for Brain MRI Images using Fuzzy Logic

I.T.

Image processing techniques are widely used in different medical field for improving early detection of disease. Early detection is necessary for discover the disease at initial stage and giving a proper treatment for that. 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 pre-processing, 2. Clustering (Fuzzy c-means clustering (FCM), Geostatistical Possibilisticclustering (GPC) and Geostatistical Fuzzy clustering (GFCM)). 1 st method of white matter segmentation is FCM (Fuzzy c-means clustering) and it is based on fuzzy logic. 2 nd method is GPC (Geostatistical Possibilistic Clustering) and it is based on possibilistic approach. 3 rd method is GFCM (Geostatistical Fuzzy c-means Clustering)and it is based on fuzzy logic and possibilistic approach.The detection results reveal that GFCM better localizes the largeregions of lesions and gives less false positive rate when compared to FCM and GPC which captures thelargest loads of WMLs only in the upper ventral horns of the brain.

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