Authors:
Yongjian Yu
1
and
Jue Wang
2
Affiliations:
1
Axon Connected, LLC, Earlysville, VA 22936, U.S.A.
;
2
Department of Mathematics, Union College, Schenectady, NY 12308, U.S.A.
Keyword(s):
Detection, Segmentation, Multiscale, Trichomoniasis, Trichomonas Vaginalis, Fluorescence Microscopy.
Abstract:
Trichomonas vaginalis (TV) causes sexually transmitted infections that, if unresolved timely, can lead to adverse health conditions. We construct a software platform integrating a novel, robust multiscale image analysis pipeline for automatic detection and characterization of TV from dual-resolution, multi-band digital fluorescence microscopy scans. We develop two spectral indices to highlight the TV in the spectrally contaminated image. The system employs a search algorithm that incorporates the spectral indices to locate the microorganisms from the low-resolution scans across the sample slide, and then identifies the TV using a multiscale edge-sensitive automatic thresholding segmentation and index-driven ranking in the high-resolution view. Method capability is demonstrated through the discriminability in the feature classification and in the TV test pipeline, both showing a high sensitivity. This technique can be used to enable automatic, fast diagnosis of trichomoniasis at the p
oint-of-care clinics.
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