Automatic skin lesion segmentation based on texture analysis and supervised learning

Automatic skin lesion detection is a key step in computer-aided diagnosis (CAD) of Skin cancers, since the accuracy of the subsequent steps in CAD crucially depends on it. In this paper, a novel method of automatic skin lesion segmentation based on texture analysis and supervised learning is proposed. It firstly involve the clustering of training image into homogeneous regions using Mean-shift; then fusion texture feature are extracted from each clustered region based on Gabor and GLCM feature; next, the classifier model is generated through supervised learning base on LIBSVM; finally, lesion regions of the unseen image are automatically predicted out by produced classifier. Comprehensive experiments have been performed on a dataset of 125 dermoscopy images. The proposed method is compared with three state-of-the-art methods and results demonstrate that the presented method achieves both robust and accurate lesion segmentation in dermoscopy images.