Automatic segmentation of skin lesion images using evolution strategies

Skin cancer is the most common form of cancer and represents 50%50% of all new cancers detected each year. However, if detected at an early stage, simple and economic treatments can cure most skin cancers. Accurate skin lesion segmentation is critical in automated skin cancer early detection and diagnosis systems. In this paper, we present an evolution strategy (ES) based segmentation algorithm to identify the lesion area within an ellipse. The method is applied to a set of 51 crosspolarization and 60 transillumination images segmented manually by a dermatologist, which are used as ground truth. Unlike most segmentation procedures, the proposed ES-based method can detect the lesion automatically without setting parameters and initial values by trial and error. Compared to results from our previous work, the ES-based method gives comparable accuracy for easily segmented images and much better results for images with either higher noise level, less prominent edges, or very small size lesions.