Efficient Melanoma Detection Using Texture-Based RSurf Features

Melanoma is the most dangerous form of skin cancer. It develops from the melanin-producing cells known as melanocytes. If melanoma is recognized and treated early, it is almost always curable. However, in early stages, melanomas are similar to benign lesions known as moles, which also originate from melanocytes. Therefore, much effort is put on the correct automated recognition of melanomas. Current computer-aided diagnosis relies on the use of various sets of colour and/or texture features. In this contribution, we present a fully automated melanoma recognition system, which employs a single set of texture-based RSurf features. The experimental evaluation demonstrates promising results and indicates strong discrimination power of these features for melanoma recognition tasks.