Computer-Supported Diagnosis of Melanoma in Profilometry

H. Handels; Th. Roß; J. Kreusch; H. H. Wolff; S. J. Pöppl

doi:10.1055/s-0038-1634144

Methods of Information in Medicine, Inhaltsverzeichnis

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

¹Institute for Medical Informatics, Germany

,

Th. Roß

¹Institute for Medical Informatics, Germany

,

J. Kreusch

²Department of Dermatology, Medical University of Lübeck, Lübeck, Germany

,

H. H. Wolff

²Department of Dermatology, Medical University of Lübeck, Lübeck, Germany

,

S. J. Pöppl

¹Institute for Medical Informatics, Germany

› Institutsangaben

Abstract

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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Referenzen

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