Limits...
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

Overview of the analysis scheme.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4663297&req=5

fig4: Overview of the analysis scheme.

Mentions: The developed image-analysis scheme (overview shown in Figure 4) is divided into the stages of image preprocessing and segmentation, parameterization, and object classification. The open-source image-processing framework OpenCV [14] is used for the implementation. Furthermore, a graphical user interface was developed to load image data and to visualize the results.


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)

Overview of the analysis scheme.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: Overview of the analysis scheme.
Mentions: The developed image-analysis scheme (overview shown in Figure 4) is divided into the stages of image preprocessing and segmentation, parameterization, and object classification. The open-source image-processing framework OpenCV [14] is used for the implementation. Furthermore, a graphical user interface was developed to load image data and to visualize the results.

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