Novel feature extraction methodology based on histopathalogical images and subsequent classification by support vector machine

A novel methodology for automatic feature extraction from histo-pathological images and subsequent classification is presented. The proposed automated system use a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. The extracted features are reduced by using sequential feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve a good result with classification accuracy of (81)%, sensitivity of(76)% and specificity of (100)%and 17 times faster than some of the reported results.