Multispectral skin patterns analysis using fractal methods

Melanoma is widely known as one of the most dangerous cancers. Over the past few decades, technological improvements have made it possible to introduce more advanced diagnostic tools for melanoma. Unfortunately, even though better tools are available, diagnosis accuracy is still unsatisfactory. Hundreds of papers have been published containing ideas on how to improve melanoma diagnosis accuracy, including a range of imaging and image analysis techniques. Some of the best diagnosis results are obtained using multi-level SIAscope images, but even with this method there is still room for further improvement. In this paper, we propose the use of additional discriminative features such as box dimension and lacunarity calculated based on a multilevel image database. The goal of this paper is to show the usefulness of fractal methods used with multilevel images and binarization methods in skin cancer pattern recognition. The results were compared to an assessment of each feature of Hunter's scoring method, which is commonly used as a diagnostic indicator by doctors. The results indicate the usefulness of the fractal characteristics of the geometric shapes of lesions or specific parts of them. Compared to other research, the presented results clearly indicate that fractal lesion characteristics can be used as one of the features taken into account in the diagnostic process.