Automated saliency-based lesion segmentation in dermoscopic images.

The segmentation of skin lesions in dermoscopic images is considered as one of the most important steps in computer-aided diagnosis (CAD) for automated melanoma diagnosis. Existing methods, however, have problems with over-segmentation and do not perform well when the contrast between the lesion and its surrounding skin is low. Hence, in this study, we propose a new automated saliency-based skin lesion segmentation (SSLS) that we designed to exploit the inherent properties of dermoscopic images, which have a focal central region and subtle contrast discrimination with the surrounding regions. The proposed method was evaluated on a public dataset of lesional dermoscopic images and was compared to established methods for lesion segmentation that included adaptive thresholding, Chan-based level set and seeded region growing. Our results show that SSLS outperformed the other methods in regard to accuracy and robustness, in particular, for difficult cases.