Abstract
Automated diagnosis of skin cancer can be easily achieved only by effective segmentation of skin lesion. But this is a highly challenging task due to the presence of intensity variations in the images of skin lesions. The authors here, have presented a histogram analysis based fuzzy C mean threshold technique to overcome the drawbacks. This not only reduces the computational complexity but also unifies advantages of soft and hard threshold algorithms. Calculation of threshold values even the presence of abrupt intensity variations is simplified. Segmentation of skin lesions is easily achieved, in a more efficient way in the following algorithm. The experimental verification here is done on a large set of skin lesion images containing every possible artifacts which highly contributes to reversed segmentation outputs. This algorithm efficiency was measured based on a comparison with other prominent threshold methods. This approach has performed reasonably well and can be implemented in the expert skin cancer diagnostic systems
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