ully Automated GrowCut-based Segmentation of Melanoma in Dermoscopic Images

Currently, diagnosis of melanoma is done primarily by biopsy, an invasive and costly procedure (American Cancer Society, 2017). A new technique called dermoscopy has been proposed as a pre-biopsy melanoma risk evaluation tool that can be applied by a wide range of physicians, including family practice doctors (Herschorn, 2012). Dermoscopy is a non-invasive method that uses microscopes to amplify details of skin lesion photographs, such as the colors and microstructures of the skin, the dermoepidermal junction (the area of tissue joining the epidermal and dermal layers of skin), and the papillary dermis (uppermost layer of the dermis). Compared to inspection of cutaneous lesions by the naked eye, this method can increase physicians’ confidence in their referral accuracy to dermatologists thereby reducing unnecessary biopsies. The early phases of malignant melanoma, however, share many clinical features with atypical or unusual looking non-malignant moles, also known as dysplastic nevi. As a result, diagnostic accuracy has been shown to range between 50-75% (Stanganelli, 2017). A commonly used standard for pre-biopsy melanoma risk evaluation is described by the ABCDE rule (Abbasi et al., 2004). This rule defines five common characteristics of malignant lesions: asymmetry (A), border irregularity (B), multiple colors (C), diameter greater than six millimeters (D), and enlargement (E). By looking for these five characteristics in dermoscopic images, physicians can evaluate the risks of malignancy of the lesions. Such evaluation, however, is inherently imperfect due to differences in human interpretation of the lesions. To enable faster, more accessible, and more effective evaluation of melanoma risks, there is a need for automatic computerized processing of skin lesions. The Fully Automated GrowCut-based Segmentation of Melanoma in Dermoscopic Images