Abstract

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.

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

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)

2078-2489

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2017 MDPI AG, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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