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Skin cancer is the most common form of cancer in the United States. Although melanoma accounts for just
11% of all types of skin cancer, it is responsible for most of the deaths, claiming more than 7910 lives
annually. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are
benign. The application of pattern recognition 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 that identify them. Purpose: This study sought to 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, using the Computer Vision
and Image Processing Tools (CVIPtools) software package. Methods: Due to changes in ambient lighting
during the photographic process, color differences between images can occur. These differences were
minimized by capturing dermoscopic images instead of photographic images. Differences in skin color
between patients were minimized via image color normalization, by converting original color images to
relative-color images. Relative-color images also helped minimize changes in color that occur due to
changes in the photographic and digitization processes. Tumors in the relative-color images were
segmented and morphologically filtered. Filtered, relative-color, tumor features were then extracted and
various pattern-classification schemes were applied. Results: Experimentation resulted in four useful
pattern classification methods, the best of which was an overall classification rate of 100% for melanoma
and melanoma in situ (grouped) and 60% for Clark nevus. Conclusion: Melanoma and melanoma in situ
have feature parameters and feature values that are similar enough to be considered one class of tumor that
significantly differs from Clark nevus. Consequently, grouping melanoma and melanoma in situ together
achieves the best results in classifying and automatically differentiating melanoma from Clark nevus
lesions.
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