A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model
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.
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