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Enhanced 3D curvature pattern and melanoma diagnosis

Warr, Robert; Zhou, Yu; Smith, Melvyn; Smith, Lyndon; Farooq, Abdul

Authors

Robert Warr

Yu Zhou

Profile Image

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

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

Abdul Farooq Abdul2.Farooq@uwe.ac.uk
Associate Director (Human-Centric Robotics)



Abstract

This article describes an enhanced curvature pattern based melanoma diagnosis system using convolution techniques and ensemble classifiers. We extract the 3. D data of melanoma with a photometric stereo device first. Then differential forms of the melanoma surface can be extracted with the convolution method proposed. After extracting 3. D based differential forms, statistical moments of enhanced principal curvatures of skin surfaces are calculated to describe the geometrical texture patterns. Finally, ensemble classifiers are constructed whose optimal mean sensitivity and specificity can reach 89.24 percent and 87.62 percent respectively. Comparisons with skin tilt/slant pattern based 3. D shape characterization method and 2. D methods like color variation and border irregularity are also included. © 2010 Elsevier Ltd.

Citation

Warr, R., Zhou, Y., Smith, M., Smith, L., & Farooq, A. (2011). Enhanced 3D curvature pattern and melanoma diagnosis. Computerized Medical Imaging and Graphics, 35(2), 155-165. https://doi.org/10.1016/j.compmedimag.2010.10.004

Journal Article Type Article
Publication Date Mar 1, 2011
Deposit Date Nov 27, 2012
Publicly Available Date Mar 28, 2024
Journal Computerized Medical Imaging and Graphics
Print ISSN 0895-6111
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 35
Issue 2
Pages 155-165
DOI https://doi.org/10.1016/j.compmedimag.2010.10.004
Keywords Shape analysis, melanoma, convolution, curvature, ensemble classifier
Public URL https://uwe-repository.worktribe.com/output/974280
Publisher URL http://dx.doi.org/10.1016/j.compmedimag.2010.10.004