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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 851014, 9 pages
Research Article

Image-Processing Scheme to Detect Superficial Fungal Infections of the Skin

1Institute of Medical Physics and Radiation Protection, Technische Hochschule Mittelhessen - University of Applied Sciences, 35390 Giessen, Germany
2Helmut Hund GmbH, Artur Herzog Straße 2, 35580 Wetzlar, Germany
3Institute of Bioprocess Engineering and Pharmaceutical Technology, Technische Hochschule Mittelhessen - University of Applied Sciences, 35390 Giessen, Germany
4Department of Dermatology, Venereology and Allergology, Justus Liebig University Giessen, 35390 Giessen, Germany

Received 12 August 2015; Revised 16 October 2015; Accepted 21 October 2015

Academic Editor: Edite Figueiras

Copyright © 2015 Ulf Mäder et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The incidence of superficial fungal infections is assumed to be 20 to 25% of the global human population. Fluorescence microscopy of extracted skin samples is frequently used for a swift assessment of infections. To support the dermatologist, an image-analysis scheme has been developed that evaluates digital microscopic images to detect fungal hyphae. The aim of the study was to increase diagnostic quality and to shorten the time-to-diagnosis. The analysis, consisting of preprocessing, segmentation, parameterization, and classification of identified structures, was performed on digital microscopic images. A test dataset of hyphae and false-positive objects was created to evaluate the algorithm. Additionally, the performance for real clinical images was investigated using 415 images. The results show that the sensitivity for hyphae is 94% and 89% for singular and clustered hyphae, respectively. The mean exclusion rate is 91% for the false-positive objects. The sensitivity for clinical images was 83% and the specificity was 79%. Although the performance is lower for the clinical images than for the test dataset, a reliable and fast diagnosis can be achieved since it is not crucial to detect every hypha to conclude that a sample consisting of several images is infected. The proposed analysis therefore enables a high diagnostic quality and a fast sample assessment to be achieved.