Human Skin Cancer Recognition and Classification by Unified Skin Texture and Color Features

In this paper we have proposed a novel method called automatic segmentation of skin lesion in conventional macroscopic images. Many approaches have been proposed to determine the skin cancer. An extensive literature survey is done to study the state-of-art techniques for skin cancer recognition; level set active contours (LSAC), skin lesion segmentation (SLS) and multidirectional gradient vector flow (MGVF) have given considerable results. A technique based on stochastic region merging (SRM) and region adjacency graph (RAG) is adopted in the proposed method. Segmenting the skin lesion from macroscopic images is a very challenging problem due to some factor such as, illumination variation, presence of hair, irregular skin color variation and multiple unhealthy skin regions. To solve all these factors we have introduced a new approach called novel iterative stochastic region merging likelihood for segmenting the skin lesion from macroscopic images based on the discrete wavelet transformation (DWT).