MR brain tumor detection employing Laplacian Eigen maps and kernel support vector machine

An innovative and robust image segmentation approach has been proposed for magnetic resonance (MR) brain tumor extraction. We have proposed a novel technique to classify a given MR brain image as benign or malignant. In order to extract the features from given MR brain tumor image, we have first employed wavelet transform which is then followed by Laplacian Eigen maps (LE) so as to curtail the dimensions of extracted features. These reduced features are now given to kernel support vector machine (K-SVM). Once we are done with classification, the next logical step remains image segmentation. We have the proposed algorithm with Gaussian Radial Basis (GRB) kernel owing to the fact that it achieves higher efficiency. Moreover, we have adopted the Leave-one-out cross validation (LOOCVCV) strategy so as to enhance generalization of K-SVM. Experimental findings reveal that our proposed algorithm outperformed the existing brain tumor extraction techniques in terms of computational and qualitative aspect. It could serve doctors to examine whether the tumors is benign or malignant.