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Biological indexes based reflectional asymmetry for classifying cutaneous lesions

Liu, Zhao; Smith, Lyndon; Sun, Jiuai; Smith, Melvyn; Warr, Robert

Authors

Zhao Liu

Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine

Jiuai Sun

Profile Image

Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof

Robert Warr



Abstract

This paper proposes a novel reflectional asymmetry descriptor to quantize the asymmetry of the cutaneous lesions for the discrimination of malignant melanoma from benign nevi. A pigmentation elevation model of the biological indexes is first constructed, and then the asymmetry descriptor is computed by minimizing the histogram difference of the global point signatures of the pigmentation model. Melanin and Erythema Indexes are used instead of the original intensities in colour space to characterize the pigmentation distribution of the cutaneous lesions. 311 dermoscopy images are used to validate the algorithm performance, where 88.50% sensitivity and 81.92% specificity have been achieved when employing an SVM classifier. © 2011 Springer-Verlag.

Citation

Liu, Z., Smith, L., Sun, J., Smith, M., & Warr, R. (2011). Biological indexes based reflectional asymmetry for classifying cutaneous lesions. Lecture Notes in Artificial Intelligence, 6893 LNCS(PART 3), 124-132. https://doi.org/10.1007/978-3-642-23626-6_16

Journal Article Type Conference Paper
Conference Name Medical Image Computing and Computer-Assisted Intervention
Conference Location Canada
Publication Date Oct 11, 2011
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Peer Reviewed Not Peer Reviewed
Volume 6893 LNCS
Issue PART 3
Pages 124-132
Book Title Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011
DOI https://doi.org/10.1007/978-3-642-23626-6_16
Public URL https://uwe-repository.worktribe.com/output/1437070
Publisher URL http://dx.doi.org/10.1007/978-3-642-23626-6_16