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Comput Math Methods Med. 2015;2015:851014. doi: 10.1155/2015/851014. Epub 2015 Nov 16.

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

Author information

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

Abstract

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.

PMID:
26649072
PMCID:
PMC4663297
DOI:
10.1155/2015/851014
[Indexed for MEDLINE]
Free PMC Article

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