Automatic Skin Lesion Segmentation Based on Supervised Learning

The accuracy of automatic skin lesion detection is important in the computer-aided diagnosis (CAD) of skin cancers. In this paper, a novel method of automatic skin lesion segmentation to get the accurate border is proposed. The initial lesion is extracted by the Otsu´s threshold firstly. Secondly, the outer peripheral region around the initial lesion is obtained with the affinity propagation clustering method (AP). The outer periphery is divided into small homogeneous sub-regions using simple linear iterative clustering (SLIC). Finally, the homogeneous sub-regions are classified into the background skin and lesion by supervised learning and the accuracy border is obtained. A series of experiments done on the proposed method and the other four state-of-the-art automatic methods show that the proposed method delivers better accuracy and robust segmentation results.