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

الملخص EN

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.