Statistics-based Classification Approach for Hyperspectral Dermatologic Data Processing

Hyperspectral Imaging (HSI) for dermatology applications lacks a physical model to differentiate between cancerous or non-cancerous pigmented skin lesions. In this paper the statistical properties of a set of HSI data are exploited as an alternative to this limitation. The hyperspectral dermatologic database employed in the experiments is composed by 40 noncancerous and 36 cancerous pigmented skin lesions (PSLs) obtained from 61 patients. The preliminary experiments suggest the potential of a simple statistics metrics, such as the coefficient of variation, to distinguish between cancerous and non-cancerous PSLs using hyperspectral data. A sensitivity result of 100% was achieved in the test set providing an overall accuracy classification of 80%.