New approach for automatic recognition of melanoma in profilometry: optimized feature selection using genetic algorithms

Heinz Handels; Th Ross; J. Kreusch; H. H. Wolff; S. J. Poeppl

doi:10.1117/12.310948

24 June 1998 New approach for automatic recognition of melanoma in profilometry: optimized feature selection using genetic algorithms

Heinz Handels, Th Ross, J. Kreusch, H. H. Wolff, S. J. Poeppl

Author Affiliations +

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310948
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

A new approach to computer supported recognition of melanoma and naevocytic naevi based on high resolution skin surface profiles is presented. Profiles are generated by sampling an area of 4 X 4 mm² at a resolution of 125 sample points per mm with a laser profilometer at a vertical resolution of 0.1 micrometers . With image analysis algorithms Haralick's texture parameters, Fourier features and features based on fractal analysis are extracted. In order to improve classification performance, a subsequent feature selection process is applied to determine the best possible subset of features. Genetic algorithms are optimized for the feature selection process, and results of different approaches are compared. As quality measure for feature subsets, the error rate of the nearest neighbor classifier estimated with the leaving-one-out method is used. In comparison to heuristic strategies and greedy algorithms, genetic algorithms show the best results for the feature selection problem. After feature selection, several architectures of feed forward neural networks with error back-propagation are evaluated. Classification performance of the neural classifier is optimized using different topologies, learning parameters and pruning algorithms. The best neural classifier achieved an error rate of 4.5% and was found after network pruning. The best result in all with an error rate of 2.3% was obtained with the nearest neighbor classifier.

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Heinz Handels, Th Ross, J. Kreusch, H. H. Wolff, and S. J. Poeppl "New approach for automatic recognition of melanoma in profilometry: optimized feature selection using genetic algorithms", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310948

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