Yi Ding
Obtaining malignant melanoma indicators through statistical analysis of 3D skin surface disruptions
Ding, Yi; Smith, Lyndon; Smith, Melvyn; Sun, Jiuai; Warr, Robert
Authors
Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine
Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof
Jiuai Sun
Robert Warr
Abstract
Background/purpose: It has been observed that disruptions in skin patterns are larger for malignant melanoma (MM) than benign lesions. In order to extend the classification results achieved for 2D skin patterns, this work intends to investigate the feasibility of lesion classification using 3D skin surface texture, in the form of surface normals acquired from a previously built six-light photometric stereo device. Material and methods: The proposed approach seeks to separate MM from benign lesions through analysis of the degree of surface disruptions in the tilt and slant direction of surface normals, so called skin tilt pattern and skin slant pattern. A 2D Gaussian function is used to simulate a normal region of skin for comparison with a lesion's observed tilt and slant patterns. The differences associated with the two patterns are estimated as the disruptions in the tilt and slant pattern respectively for lesion classification. Results: Preliminary studies on11 MMs and 28 benign lesions have given Receiver operating characteristic areas of 0.73 and 0.85 for tilt and slant pattern, respectively, which are better than 0.65 previously obtained for the skin line direction using the same samples. Conclusions: This paper has demonstrated an important application of 3D skin texture for computer-assisted diagnosis of MM in vivo. By taking advantage of the extra dimensional information, preliminary studies suggest that some improvements over the existing 2D skin line pattern approach for the differentiation between MM and benign lesions. © 2009 John Wiley & Sons A/S.
Journal Article Type | Article |
---|---|
Publication Date | Jul 23, 2009 |
Deposit Date | Sep 27, 2010 |
Publicly Available Date | Apr 12, 2016 |
Journal | Skin Research and Technology |
Print ISSN | 0909-752X |
Electronic ISSN | 1600-0846 |
Publisher | Wiley |
Peer Reviewed | Peer Reviewed |
Volume | 15 |
Issue | 3 |
Pages | 262-270 |
DOI | https://doi.org/10.1111/j.1600-0846.2009.00352.x |
Keywords | 3D skin texture, a reference skin model, 2D Gaussian function, skin tilt pattern, skin slant pattern |
Public URL | https://uwe-repository.worktribe.com/output/993917 |
Publisher URL | http://dx.doi.org/10.1111/j.1600-0846.2009.00352.x |
Contract Date | Apr 12, 2016 |
Files
Lyndon SRT non-copyright paper.pdf
(1.7 Mb)
PDF
You might also like
Machine vision and deep learning for robotic harvesting of shiitake mushrooms
(2024)
Presentation / Conference Contribution
3D Machine vision and deep learning for enabling automated and sustainable assistive physiotherapy
(2024)
Presentation / Conference Contribution
Estimating water storage from images
(2024)
Presentation / Conference Contribution
Maize yield predictive models and mobile-based decision support system for smallholder farmers in Africa
(2022)
Presentation / Conference Contribution
A robust machine learning framework for diabetes prediction
(2021)
Presentation / Conference Contribution
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search