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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

Image of a singular (a) and clustered (b) hyphae using the automated fluorescence imaging system.
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fig1: Image of a singular (a) and clustered (b) hyphae using the automated fluorescence imaging system.

Mentions: The fluorescence images of the samples captured with the automated device typically show a dark background with bright fluorescent structures. These structures are either hyphae that belong to a fungal infection or false-positive structures and artefacts that can be misinterpreted as hyphae. Figure 1 shows the two classes of hyphae, singular (a) and clustered (b), which can be generally observed and which were under investigation in this study. Singular hyphae are characterized by relatively uniform width and intensity. The shape is often elongated or curved without branches. The clustered hyphae can be described as an agglomeration of overlapping singular and branched hyphae that may have multiple junctions.


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)

Image of a singular (a) and clustered (b) hyphae using the automated fluorescence imaging system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663297&req=5

fig1: Image of a singular (a) and clustered (b) hyphae using the automated fluorescence imaging system.
Mentions: The fluorescence images of the samples captured with the automated device typically show a dark background with bright fluorescent structures. These structures are either hyphae that belong to a fungal infection or false-positive structures and artefacts that can be misinterpreted as hyphae. Figure 1 shows the two classes of hyphae, singular (a) and clustered (b), which can be generally observed and which were under investigation in this study. Singular hyphae are characterized by relatively uniform width and intensity. The shape is often elongated or curved without branches. The clustered hyphae can be described as an agglomeration of overlapping singular and branched hyphae that may have multiple junctions.

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