Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images

Table 1

Comparison of different brain MRI segmentation methods [81, 82] along with method proposed by the authors [83] based upon pixel classification and clustering classified by the region of interest being segmented.

Region of interest	Method	Procedure
Brain tumors	k-means + fuzzy c-means	Pixel intensity k-means followed by pixel intensity and membership-based fuzzy c-means clustering with preprocessing using median filters and postprocessing using feature extraction and approximate reasoning
Brain lesions	Fuzzy c-means with edge filtering and watershed	Pixel intensity and membership-based fuzzy c-means with preprocessing using thresholding techniques and postprocessing using edge filtering and watershed techniques
Gray and white matter regions	Adaptive fuzzy c-means (proposed method in this work)	Pixel intensity and membership-based fuzzy c-means clustering with preprocessing using elliptical Hough transform and postprocessing using connected region analysis