Tumor Detection in Brain Using MRI Images

Abstract

This work provides a robust segmentation approach that combines Template-based K-means with the modified Fuzzy C-means (TKFCM) clustering algorithm. Operator and equipment error is reduced. The template is chosen in this method based on the convolution of grey level intensity in a tiny area of a brain image and a brain tumor image. The K-means method emphasizes initial segmentation through template selection. Distances from the cluster centroid to the cluster data points are used to update membership until it is optimal. This Euclidian distance is determined by the coarse image's intensity, entropy, contrast, dissimilarity, and homogeneity which were solely based on resemblance in conventional FCM. Then, using updated membership and automatic cluster selection, a sharp segmented image with red indicated tumor is created using the improved FCM technique. TKFCM detects minor differences in grey level intensity between normal and diseased tissue. The TKFCM method's performance is analyzed using a neural network to produce better regression and less error. The performance parameters yield meaningful results that are helpful at finding cancers in several different intensity-based brain scans brain. Keywords - Magnetic Resonance Imaging (MRI), Revised Fuzzy C-Means Algorithms and Template based K-Mean Clustering (TKFCM), gray level intensity, coarse image, features selection, Artificial Neural Network (ANN).