Integrating soft and hard threshold selection algorithms for accurate segmentation of skin lesion

Accurate segmentation of skin lesion is one of the most important step for automated diagnosis of skin cancer. Various characteristics of skin lesions and intensity variations in images can make it a highly challenging task. A new histogram analysis based fuzzy C mean thresholding method is presented here. It unifies the advantages of soft and hard thresholding algorithms along with reducing the computational complexity. Appropriate threshold value can be calculated even in the presence of abrupt intensity variations. This algorithm shows significantly improved performance for the segmentation of skin lesions. Experimental verification is done on a large set of skin lesion images having almost all types of expected artifacts that may badly affect the segmentation results. Performance evaluation is done by comparing the diagnosis results based on this method with other state of the art thresholding methods. Results show that the proposed approach performs reasonably well and can form a basis of expert diagnostic systems for skin cancer.