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Automated melanoma recognition

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6 Author(s)
Ganster, H. ; Inst. for Comput. Graphics & Vision, Tech. Univ. Graz, Austria ; Pinz, A. ; Rohrer, R. ; Wildling, E.
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A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.

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Medical Imaging, IEEE Transactions on  (Volume:20 ,  Issue: 3 )