ABCD rules segmentation on Malignant tumor and Benign skin lesion images

Abstract

Skin lesion is defined as a superficial growth or patch of the skin that is visually different than its surrounding area. Skin lesions appear for many reasons such as the symptoms indicative of diseases, birthmarks, allergic reactions, and so on. Images of skin lesions are analyzed by computer to capture certain features to be characteristic of skin diseases. These activities can be defined as automated skin lesion diagnosis (ASLD). ASLD involves five steps including image acquisition, pre-processing to remove occluding artifacts (such as hair), segmentation to extract regions of interest, feature selection and classification. This paper present analysis of automated segmentation called the ABCD rules (Asymmetry, Border irregularity, Color variegation, Diameter) in image segmentation. The experiment was carried on Malignant tumor and Benign skin lesion images. The study shows that the ABCD rules has successfully classify the images with high value of total dermatoscopy score (TDS). Although some of the analysis shows false alarm result, it may give the significant input to search suitable segmentation measure.