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.
H. A. Almubarak et al., "Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images," Information (Switzerland), vol. 8, no. 3, MDPI AG, Jul 2017.
The definitive version is available at https://doi.org/10.3390/info8030089
Electrical and Computer Engineering
Keywords and Phrases
Color; Computer Circuits; Dermatology; Diagnosis; Fuzzy Clustering; Graphic Methods; Image Processing; Oncology; Color Clustering; Color Features; Color Histogram; Dermoscopy Images; False Positive; Histogram; Malignant Melanoma; Set Cardinality; Fuzzy Logic
International Standard Serial Number (ISSN)
Article - Journal
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