Fuzzy Logic Techniques for Blotch Feature Evaluation in Dermoscopy Images
Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%
A. Khan et al., "Fuzzy Logic Techniques for Blotch Feature Evaluation in Dermoscopy Images," Computerized Medical Imaging and Graphics, vol. 33, no. 1, pp. 50-57, Elsevier, Jan 2009.
The definitive version is available at https://doi.org/10.1016/j.compmedimag.2008.10.001
Electrical and Computer Engineering
National Institute of Health (U.S.)
Keywords and Phrases
Asymmetric Blotches; Dermoscopy; Malignant Melanoma; Neural Network; Fuzzy Logic; Image Analysis; Fuzzy logic; Image analysis
International Standard Serial Number (ISSN)
Article - Journal
© 2009 Elsevier, All rights reserved.