Brain Image Segmentation and Tumour Detection using Adaptive Clustering and RBF-SVM Classifier
Publication Date : 23/04/2016
Brain tumour is a group of abnormal cells that grows inside of the brain or around the brain. Tumours can directly destroy all healthy brain cells. In order to detect the tumour part, image segmentation plays a significant role in computer vision systems. It aims at extracting meaningful objects lying within the image. Generally, there is no particular system or method for image segmentation. Clustering is a robust approach that has been reached in image segmentation. The cluster analysis is to partition image information set into a number of disjoint groups or clusters. In this work we used adaptive k-means clustering for segmentation, this is one among the standard methods because of its simplicity and computational efficiency, GLDM (Grey Level Difference Method) for features extraction and interpolation for feature selection. RBF-SVM (Radial Basis Function Support Vector Machine) classifier is used for the classification of the brain tumor part in image. Keywords-Adaptive k-means Clustering, GLDM features, RBF-SVM Classifier.
No. of Downloads :