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Methods Inf Med 1999; 38(01): 43-49
DOI: 10.1055/s-0038-1634144
Original article
Schattauer GmbH

Computer-Supported Diagnosis of Melanoma in Profilometry

H. Handels
1   Institute for Medical Informatics, Germany
,
Th. Roß
1   Institute for Medical Informatics, Germany
,
J. Kreusch
2   Department of Dermatology, Medical University of Lübeck, Lübeck, Germany
,
H. H. Wolff
2   Department of Dermatology, Medical University of Lübeck, Lübeck, Germany
,
S. J. Pöppl
1   Institute for Medical Informatics, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
08 February 2018 (online)

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Abstract:

Laser profilometry offers new possibilities to improve non-invasive tumor diagnostics in dermatology. In this paper, a new approach to computer-supported analysis and interpretation of high-resolution skin-surface profiles of melanomas and nevocellular nevi is presented. Image analysis methods are used to describe the profile‘s structures by texture parameters based on co-occurrence matrices, features extracted from the Fourier power spectrum, and fractal features. Different feature selection strategies, including genetic algorithms, are applied to determine the best possible subsets of features for the classification task. Several architectures of multilayer perceptrons with error back-propagation as learning paradigm are trained for the automatic recognition of melanomas and nevi. Furthermore, network-pruning algorithms are applied to optimize the network topology. In the study, the best neural classifier showed an error rate of 4.5% and was obtained after network pruning. The smallest error rate in all, of 2.3%, was achieved with nearest neighbor classification.

Keywords:

Laser Profilometry - Image Analysis - Feature Selection - Pattern Recognition - Artificial Neural Networks
 

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