An efficient neuro-fuzzy based segmentation of normal tissues in brain MRI (BMRI) using extensive feature set - Bhanumurthy, Yaswanth; Koteswararao, Anne (British Journal of Healthcare and Medical Research, 2014)
Brain tissue Segmentation from the MRI images is having significance in the medical research field. The accurate Segmentation of the normal as well as the abnormal tissues is the complex assignment in this process. In this paper, a technique named Neuro-Fuzzy Based Segmentation (NFBS) is proposed for segmenting the normal features such as White Matter (WM), Gray Matter (GM) and Cerebro-Spinal Fluid (CSF) in the MRI Brain images. (1) Feature extraction (2) Classification (3) Segmentation are the three stages offered in this work. At first, the features such as energy, entropy, homogeneity, contrast and correlation from MRI Brain Images are extracted. Next, by utilizing Neuro-Fuzzy classifier, the Classification process is carried out and for this process, the feature set is specified as the input. From the outcome of Classification, the images are categorized into normal as well as abnormal. The further procedure Segmentation is performed according to this outcome only. The normal MRI images are segmented into normal tissues like White Matter (WM), Gray Matter (GM) and Cerebro-Spinal Fluid (CSF). All the tissues are individually segmented by special methods such as Gradient method, Orthogonal Polynomial Transform method. Utilizing MATLAB platform, the implementation of the proposed technique is made. The experimentation is carried out on the MRI Brain Images by BrainWeb data sets. The performance of the proposed technique is assessed with the help of the metrics namely FPR, FNR, Specificity, Sensitivity and Accuracy. Therefore, using our proposed techniques with enhanced classification, the normal tissues of MRI Brain images are segmented accurately.