Machine vision 3D skin texture analysis for detection of melanoma

L.N. Smith (Machine Vision Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, UK)
M.L. Smith (Machine Vision Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, UK)
A.R. Farooq (Machine Vision Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, UK)
J. Sun (Machine Vision Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, UK)
Y. Ding (Machine Vision Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, UK)
R. Warr (Department of Plastic Surgery, North Bristol NHS Trust, Bristol, UK)

Sensor Review

ISSN: 0260-2288

Publication date: 29 March 2011

Abstract

Purpose

The purpose of this paper is to describe innovative machine vision methods that have been employed for the capture and analysis of 3D skin textures; and the resulting potential for assisting with identification of suspicious lesions in the detection of skin cancer.

Design/methodology/approach

A machine vision approach has been employed for analysis of 3D skin textures. This involves an innovative application of photometric stereo for the capture of the textures, and a range of methods for analysing and quantifying them, including statistical methods and neural networks.

Findings

3D skin texture has been identified as a useful indicator of skin cancer. It can be used to improve realism of virtual skin reconstructions in tele‐dermatology. 3D texture features can also be combined with 2D features to obtain a more robust classifier for improving diagnostic accuracy, thereby assisting with the long‐term goal of implementing computer‐aided diagnostics for skin cancer.

Originality/value

The device developed for capturing 3D skin textures is known as the “Skin Analyser”, and as far as the authors know it is unique in the world in being able to recover 3D textures from pigmented lesions in vivo. There currently exist numerous methods for analysing lesions, including manual inspection (using established heuristics commonly known as ABCD rules), dermoscopy and SIAoscopy. The ability to capture and analyse 3D lesion textures complements these existing techniques and forms a valuable additional indicator for assisting with the early detection of dangerous skin cancers such as melanoma.

Keywords

Citation

Smith, L., Smith, M., Farooq, A., Sun, J., Ding, Y. and Warr, R. (2011), "Machine vision 3D skin texture analysis for detection of melanoma", Sensor Review, Vol. 31 No. 2, pp. 111-119. https://doi.org/10.1108/02602281111109961

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

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