Biological indexes based reflectional asymmetry for classifying cutaneous lesions

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

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Medical Image Computing and Computer-Assisted Intervention
• 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
• 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• Peer Reviewed: Not Peer Reviewed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