Skin  cancer  is  the  most  common  type  of  cancer  and  represents  50%  all  new  cancers  detected  each  year.  The  deadliest form of skin cancer is melanoma and its incidence has been rising at a rate of 3% per year. Due to the  costs  for  dermatologists  to  monitor  every  patient,  there  is  a  need  for  an  computerized  system  to  evaluate  a  patient‘s  risk  of  melanoma  using  images  of  their  skin  lesions  captured  using  a  standard  digital  camera. Dermoscopy is an imaging technique that has been widely used in the diagnosis of skin lesions. However, its accuracy largely depends on the dermatologist’s experience; thus, computeraided diagnosis techniques are required. In this paper, a novel approach based on a deformable model is proposed to handle the segmentation of skin lesions in dermoscopic images. The RGB color space is converted so that the color information contained in the images can be used effectively to differentiate normal skin and skin lesions; and the differences in the color channels are combined together to define the speed function and the stopping criterion of the deformable model. This novel approach is robust against the noise, and provides an effective and flexible segmentation. Also, using comparative data, the reliability and the implementation issues of the approach are discussed in this paper.  Lesion injury is essential to initiate the correct first treatment. For diagnose the treatment is necessary to know the Segmentation of the Lesion. Correct visual assessment of Melanocytic Lesion relies highly on specialized dermatological expertise. Epidermal Lesions are usually red, dry, and painful. Experiment results explain the validity and feasibility of our proposed algorithm.