Abstract
Skin disease causes serious health hazards like cancer, leprosy, scleroderma, etc. Early and precise diagnosis can overcome the long-term effect of these diseases. Computer vision brings this precision with automated diagnostic system. The most crucial part of such a system is the segmentation of diseased regions. A novel segmentation algorithm is proposed in this study by comparing Tsalli’s and Sharma–Mittal entropy. Five well-known state-of-the-art segmentation techniques are compared with the selected one using Hubert and Adjusted Rand Index. The result shows that Tsalli’s entropy-based algorithm is significantly better for the pigmented lesion segmentation than the compared techniques.
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