Automated melanoma recognition

A system for the computerized analysis of images obtained from ELM has been developed to enhance the early recognition of malignant melanoma, As an in itial step, the binary mask of the skin lesion is determined by several bas ic segmentation algorithms together with a fusion strategy, A set of featur es containing shape and radiometric features as well as local and global pa rameters is calculated to describe the malignancy of a lesion, Significant features are then selected from this set by application of statistical feat ure subset selection methods. The final kNN classification delivers a sensi tivity of 87% with a specificity of 92%.