Improved Fuzzy C-Means for Brain Tissue Segmentation Using T1- Weighted MRI Head Scans

Brain tissue segmentation of Magnetic Resonance Imaging (MRI) is an important and one of the challenging tasks in medical image processing. MRI images of brain are classified into two types: classifying tissues, anatomical structures. It comprised into different tissue classes which contain four major regions, namely Gray matter (GM), White matter (WM), Cerebrospinal fluid (CSF), and Background (BG). The present study of proposed method is an improved fuzzy c-means (FCM) clustering for tissue segmentation using T1-weighted head scans. The proposed method improved by modifying the objective function, cluster center and membership value for updating criterion. The quantitative measures of results were compared using the metrics Dice Coefficient (DC) and processing time. The DC value of proposed method attained maximum value while compared to conventional FCM. The proposed method is very efficient and faster than FCM for brain tissue segmentation from T1-weighted head scans.