Title: A computer assisted diagnosis system for malignant melanoma using 3D skin surface texture features and artificial neural network

 

Author: Yi Ding, Lyndon Smith, Melvyn Smith, Jiuai Sun, Robert Warr

 

Address: Machine Vision Laboratory, Bristol Institute of Technology, University of the West of England, DuPont Building, Frenchay Campus, Bristol, BS16 1QY, UK. ' Machine Vision Laboratory, Bristol Institute of Technology, University of the West of England, DuPont Building, Frenchay Campus, Bristol, BS16 1QY, UK. ' Machine Vision Laboratory, Bristol Institute of Technology, University of the West of England, DuPont Building, Frenchay Campus, Bristol, BS16 1QY, UK. ' Machine Vision Laboratory, Bristol Institute of Technology, University of the West of England, DuPont Building, Frenchay Campus, Bristol, BS16 1QY, UK. ' Department of Plastic Surgery, North Bristol NHS Trust, Bristol, BS16 1LE, UK; Department of Dermatology, Frenchay Hospital, Bristol, UK

 

Journal: Int. J. of Modelling, Identification and Control, 2010 Vol.9, No.4, pp.370 - 381

 

Abstract: It has been observed that disruptions in skin patterns are larger for malignant melanoma than for benign lesions. In contrast to existing work on 2D skin line patterns, this work proposes a computer assisted diagnosis system for malignant melanoma based on acquiring, analysing and classifying 3D skin surface texture features. Specifically, the 3D skin surface texture, in the form of surface normal vectors are acquired from a six-light photometric stereo device, the 3D features from the surface normals are extracted as the residuals between the acquired data and those from a 2D Gaussian model, while a three-layer feedforward neural classifier is used to classify the residuals. Preliminary studies on a sample set including 12 malignant melanomas and 34 benign lesions have given 91.7% sensitivity and 76.4% specificity using the proposed 3D skin surface normal features, which are better than 91.7% sensitivity and 25.7% specificity using the existing 2D skin line pattern features over the same lesion samples. This demonstrates that the proposed computer assisted diagnosis system of malignant melanoma based on 3D features offers an improvement over that based on 2D skin line patterns.

 

Keywords: 3D skin texture; reference skin models; 2D Gaussian function; skin tilt patterns; skin slant patterns; multilayer perceptron; feature enhancement; artificial neural networks; ANNs; malignant melanoma; computer assisted diagnosis; skin cancer.

 

DOI: 10.1504/IJMIC.2010.033212

10.1504/10.33212

 

 

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