Set of descriptors for skin cancer diagnosis using non-dermoscopic color images

• Main Authors: Jafari, M. H., Samavi, S., Soroushmehr, S. M. R., Mohaghegh, H., Karimi, N., Najarian, K.
• Format: Conference Proceeding
• Language: English
• Subjects: classification; Decision support systems; feature extraction; medical imaging; Melanoma diagnosis; pigmented skin lesion
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2016.7532837

Melanoma is the deadliest form of skin cancer. Diagnosis of melanoma in early stages significantly enhances the survival rate. Recently there has been a rising trend in web-based and mobile applications for early detection of melanoma using images captured by conventional cameras. These images usually contain fewer detailed information in comparison with dermoscopic (microscopic) images. Meanwhile, non-dermoscopic images have the advantage of broad availability. In this paper a set of ten features is proposed which cover different color characteristics of melanoma visible in skin images. The first 5 features are extracted using Fuzzy C-means clustering based on color variations and color spatial distributions of pigmented skin. These features are shown to be discriminative for melanoma lesions. The next 5 features consider colors and intensity of the colors. Hence, a 10 dimensional color feature space is formed. Experimental results show that classification accuracy of suspicious moles, by the proposed set of features, outperforms comparable state-of-the-art methods.