Rainer Hoffmann-Wellenhof

Medical University of Graz

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Andreas Holzinger

Medical University of Graz

Julia Frühauf

Medical University of Graz

Josef Smolle

University of Graz

Regina Fink‐Puches

Medical University of Graz

Ingrid Wolf

Medical University of Graz

Verena Ahlgrimm‐Siess

Paracelsus Medical University

Siegbert Kaiser

Primož Kosec

University of Maribor

H. Kerl

Medical University of Graz

H. Stettner

University of Klagenfurt

Cooperative Institutions

Medical University of Graz

University of Graz

Austrian Society of Dermatology and Venereology

Graz University Hospital

Statistics Austria

Institute for Medical Informatics and Biostatistics

University of Maribor

Graz University of Technology

Medical University of Vienna

University of Zurich

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Computer-aided diagnosis of melanocytic skin tumors by use of confocal laser scanning microscopy images.

PubMed (2011)

Marco Wiltgen Marcus Bloice Sílvia Helena Koller Rainer Hoffmann-Wellenhof Josef Smolle

To check the applicability of machine learning algorithms for the computer-aided diagnosis of confocal laser scanning microscopy (CLSM) views of skin lesions.Features, based on spectral properties of the wavelet transform, are very suitable for the automatic analysis because architectural structures at different scales play an important role in diagnosis of CLSM views. The images are discriminated by several machine learning algorithms, based on Bayes-, tree-, rule-, function (numeric)-, and lazy-classifiers.The function and lazy classifiers delivered best classification results. However, these algorithms deliver no information about the inference mechanism leading to the classification. The tree classifiers provided better results than the rule classifiers. To obtain more insight into the inference process, and to compare it with the diagnostic guidelines of the dermopathologists, we combined the advantages of tree, numerical, and rule classifiers and choose the classification and regression trees (CART) algorithm, which automatically generates accurate inferring rules. The classification results were relocated to the images by use of the inferring rules as diagnostic aid.The discriminated elements of the skin lesions images show tissue with features in good accordance with typical diagnostic CLSM features.

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Computer-Aided Diagnosis

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Citations (6)

Inflammatory Infiltrate in Melanoma Assessed by Automated Image Analysis in Hematoxylin and Eosin-Stained Slides Is Not Statistically Significant in Terms of Prognosis.

American Journal of Dermatopathology (1999)

Josef Smolle Regina Fink‐Puches Rainer Hoffmann-Wellenhof H. Kerl

Eosin

10.1097/00000372-199902000-00066

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