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

M. H. Jafari, S. Samavi, S. M. R. Soroushmehr, H. Mohaghegh, Nader Karimi, K. Najarian

Research output: Chapter in Book/Conference paperConference paperpeer-review

18 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781467399616
Publication statusPublished - 3 Aug 2016
Externally publishedYes
Event2016 IEEE International Conference on Image Processing - Phoenix, United States
Duration: 25 Sept 201628 Sept 2016
Conference number: 23rd


Conference2016 IEEE International Conference on Image Processing
Abbreviated titleICIP 2016
Country/TerritoryUnited States


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