Segmentation and Classification of Dermoscopic Skin Cancer on Green Channel

Melanoma the most dangerous type of skin cancer, has been on the rise in recent years. Hands-on identification of melanoma in its early stages with the unaided eye is error-prone and necessitates extensive expertise and experience. Due to the scarcity of skilled dermatologists, a computerized and automated technique is required to effectively identify melanoma. The following approach attempts to accomplish this task by creating a new approach capable of segmenting, then classifying melanoma. The procedure begins with the preparation of dermoscopic images to remove hairs using the Dull Razor algorithm, followed by image segmentation, in which we computed the Hausdorff Distance, Dice, and Jaccard coefficients to determine which channel of the RGB space was best to utilize to separate the skin lesion from the background. The segmented images using the green channel are then utilized to calculate the Gray Level Co-occurrence Matrices (GLCM) and to extract the color characteristics of the region of interest. Our approach is able to achieve a Dice coefficient and an accuracy of 95% on the PH2 dermoscopic images.