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Information 2017, 8(3), 89;

Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA
Stoecker and Associates, 10101 Stoltz Dr., Rolla, MO 65401, USA
Author to whom correspondence should be addressed.
Received: 20 May 2017 / Revised: 21 July 2017 / Accepted: 21 July 2017 / Published: 25 July 2017
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over an eight-connected neighborhood on the outer 25% of the skin lesion with an alpha-cut of 0.08 can recognize 92.6% of melanomas with approximately 13.5% false positive lesions. These results show the critical importance of colors in the lesion periphery. Our fuzzy logic-based description of lesion colors offers relevance to clinical descriptions of malignant melanoma. View Full-Text
Keywords: image processing; dermatology; color; malignant melanoma; histogram; fuzzy logic image processing; dermatology; color; malignant melanoma; histogram; fuzzy logic

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Almubarak, H.A.; Stanley, R.J.; Stoecker, W.V.; Moss, R.H. Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images. Information 2017, 8, 89.

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