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

Mäder U, Quiskamp N, Wildenhain S, Schmidts T, Mayser P, Runkel F, Fiebich M - Comput Math Methods Med (2015)

Bottom Line: The results show that the sensitivity for hyphae is 94% and 89% for singular and clustered hyphae, respectively.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Physics and Radiation Protection, Technische Hochschule Mittelhessen - University of Applied Sciences, 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.

No MeSH data available.


Related in: MedlinePlus

Exemplary overview of clinical images. Fungal infection is indicated by elliptical markers (b, c). The rectangular marker (a) represents the extracted skin scales. False-positive structures are indicated by arrows: cellulose fiber (a), obscuring and misc. particles (b, c). Circular and irregular air inclusions are present in all images.
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fig3: Exemplary overview of clinical images. Fungal infection is indicated by elliptical markers (b, c). The rectangular marker (a) represents the extracted skin scales. False-positive structures are indicated by arrows: cellulose fiber (a), obscuring and misc. particles (b, c). Circular and irregular air inclusions are present in all images.

Mentions: Figure 3 shows an exemplary overview of the clinical images with multiple structures like skin scales, cellulose fibers, and miscellaneous particles that can often be found. The images indicate the variety of suspicious objects that have to be dealt with while detecting hyphae and the challenges in evaluating the data for dermatologists and software algorithms.


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

Mäder U, Quiskamp N, Wildenhain S, Schmidts T, Mayser P, Runkel F, Fiebich M - Comput Math Methods Med (2015)

Exemplary overview of clinical images. Fungal infection is indicated by elliptical markers (b, c). The rectangular marker (a) represents the extracted skin scales. False-positive structures are indicated by arrows: cellulose fiber (a), obscuring and misc. particles (b, c). Circular and irregular air inclusions are present in all images.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4663297&req=5

fig3: Exemplary overview of clinical images. Fungal infection is indicated by elliptical markers (b, c). The rectangular marker (a) represents the extracted skin scales. False-positive structures are indicated by arrows: cellulose fiber (a), obscuring and misc. particles (b, c). Circular and irregular air inclusions are present in all images.
Mentions: Figure 3 shows an exemplary overview of the clinical images with multiple structures like skin scales, cellulose fibers, and miscellaneous particles that can often be found. The images indicate the variety of suspicious objects that have to be dealt with while detecting hyphae and the challenges in evaluating the data for dermatologists and software algorithms.

Bottom Line: The results show that the sensitivity for hyphae is 94% and 89% for singular and clustered hyphae, respectively.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Physics and Radiation Protection, Technische Hochschule Mittelhessen - University of Applied Sciences, 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.

No MeSH data available.


Related in: MedlinePlus