Automatic segmentation of skin lesion images using evolution strategies

Skin cancer has been the most common and represents 50% of all new cancers detected each year. If detected at an early stage, simple and economic treatment can cure it mostly. Accurate skin lesion segmentation is critical in automated early skin cancer detection and diagnosis systems. In this paper, we propose an Evolution Strategies (ES) based segmentation algorithm to identify the lesion area within an ellipse. The method is applied to 51 XLM and 60 TLM images which have manual segmentation from dermatologists as TRUTH. Unlike most segmentation methods, the proposed ES-based segmentation method can detect the lesion automatically without setting parameters and initial values by trial and error. The method is also compared to algorithms developed in [13,31]. The ES-based method gives comparable accuracy for easily segmented images and much better results for images with either higher noise level, less prominent edge information, or very small size lesions.