ABCD Feature Extraction for Melanoma Screening Using Image Processing: A review
Keywords:
Melanoma, Image Processing, Feature Extraction, ABCD rulesAbstract
Melanoma is an uncommon type of skin cancer. Once cancer cells spread to internal organs, the disease becomes deadly with high mortality rate. Early diagnosis can reduce disease progression as well as death rate from melanoma. For precise diagnosis of the disease at the early stage, specialized physicians are needed. Unfortunately, only a handful of these experts are available. Therefore, applying cutting-edge technologies to melanoma screening and diagnosis can help minimizing death of patients and reduces workloads of physicians. Image processing is a popular branch of data science for differentiating images using their features, i.e., size, color, margin. Image processing composes of 5 steps, including data acquisition, data preprocessing, feature extraction, modeling, and evaluation. During feature extraction, ABCD rule, which stands for asymmetry, border, color, and diameter, respectively, is widely applied due to its popularity and accuracy. In the current review, we aimed to elaborate image processing protocol for screening of melanoma with ABCD feature extraction. The detailed methods for performing feature extraction using ABCD rules are summarized. This review would be beneficial for scientists who interest in this field, and for future development of innovation to improve health of the patients and the public.
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