Dermoscopic Feature Analysis for Melanoma Recognition and Prevention

Computer-aided diagnosis system automatically analyze skin lesions, and reduces the amount of repetitive and boring tasks carried out by the doctor. The full model of an automated system includes three important stages in order to comply with the lesion analysis : segmentation, feature extraction and classification. The data-set contains images and annotations provided by physicians. Segmentation is an imperative preprocessing step for CAD system of skin lesions. Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Dermatologists take keen interest in examining a specific clinically significant part in a lesion. That part is projected to have lesion information in the form of texture that can be relevant for detection. In case of detection of melanoma various local features for example pigment network and streaks usually occur in peripheral region of the lesion. This led to the extraction of peripheral part for feature extraction instead of whole lesion processing. In this article detailed information regarding Feature extraction and selection techniques for dermoscopic images is presented.