A Fuzzy-Based Histogram Analysis Technique for Skin Lesion Discrimination in Dermatology Clinical Images
A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermatology clinical images. The approach utilizes a fuzzy set for benign skin lesion color, and alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy ratio and fusion with a previously determined percent melanoma color feature over a data set of 258 clinical images. For the fusion technique, alpha-cuts for the fuzzy ratio can be chosen to recognize over 93.30% of melanomas with approximately 15.67% false positive lesions.
R. J. Stanley et al., "A Fuzzy-Based Histogram Analysis Technique for Skin Lesion Discrimination in Dermatology Clinical Images," Computerized Medical Imaging and Graphics, vol. 27, no. 5, pp. 387-396, Elsevier, Sep 2003.
The definitive version is available at https://doi.org/10.1016/S0895-6111(03)00030-2
Electrical and Computer Engineering
Keywords and Phrases
Approximation Theory; Algorithms; Benign Tumor; Clinical Feature; Color Histogram Analysis; Computer Assisted Diagnosis; Dermatology; Diagnosis; Diagnostic Accuracy; Diagnostic Imaging; Diagnostic Value; Differential Diagnosis; Fuzzy Logic; Fuzzy Sets; Humans; Image Analysis; Malignant Melanoma; Medical Imaging; Model; Priority Journal; Skin Defect; Skin Neoplasms; Skin Pigmentation; Skin Tumor
International Standard Serial Number (ISSN)
Article - Journal
© 2003 Elsevier, All rights reserved.