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. 2003 Sep-Oct;27(5):387-96.
doi: 10.1016/s0895-6111(03)00030-2.

A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images

R Joe Stanley  1 , Randy Hays Moss, William Van Stoecker, Chetna Aggarwal
Affiliations

A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images

R Joe Stanley et al. Comput Med Imaging Graph. 2003 Sep-Oct.

Abstract

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.

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Figures

Fig. 1
Fig. 1
Clinical image examples of benign and melanoma skin lesions: (a) melanoma image; (b) benign image (seborrheic keratosis).
Fig. 2
Fig. 2
Relative RGB space showing one bin of dimension 4 × 4 × 4 and its three color bin edges.
Fig. 3
Fig. 3
A relative secondary histogram (a) and its corresponding trapezoidal membership function for fuzzy set B (b). The frequency count F is labeled on the secondary histogram and membership function plot.
Fig. 4
Fig. 4
Example of a benign mimic consistently classified as a melanoma.
See this image and copyright information in PMC

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