Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

According to the statistics, melanoma accounts for just 11 % of all types of skin cancer, it is responsible for most of the deaths. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of image processing techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics for identifying them. This research tried find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions. The color differences between images that occur because of differences in ambient lighting during the photographic process were minimized by the use of dermoscopic images. Differences in skin color between patients was minimized by using normalizing them by means of converting them to relative-color images, and differences in ambient lighting during photography, and the photographic and digitization processes, original color images were normalized by converting them into relative-color images. Tumors in the relative-color images were then segmented out and morphologically filtered. The filtered-tumor features were then extracted and various pattern-classification schemes were applied. Experimentation resulted in four useful pattern classification methods, the best of which was a classification rate of 100% for melanoma and melanoma in situ (grouped) and 65% for Clark nevus.