Zhao Liu
Biological indexes based reflectional asymmetry for classifying cutaneous lesions
Liu, Zhao; Smith, Lyndon; Sun, Jiuai; Smith, Melvyn; Warr, Robert
Authors
Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine
Jiuai Sun
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Peer Reviewed | Not Peer Reviewed |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 |
You might also like
3D Machine vision and deep learning for enabling automated and sustainable assistive physiotherapy
(2023)
Conference Proceeding
Maize yield predictive models and mobile-based decision support system for smallholder farmers in Africa
(2022)
Presentation / Conference
Towards machine vision for insect welfare monitoring and behavioural insights
(2022)
Journal Article