Automatic Detection and Identification of Trichomonas Vaginalis from Fluorescence Microscopy Images

Yongjian Yu, Jue Wang

2022

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 point-of-care clinics.

Paper Citation

in Harvard Style

Yu Y. and Wang J. (2022). Automatic Detection and Identification of Trichomonas Vaginalis from Fluorescence Microscopy Images. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING; ISBN 978-989-758-552-4, SciTePress, pages 190-197. DOI: 10.5220/0010993400003123

in Bibtex Style

@conference{bioimaging22,
author={Yongjian Yu and Jue Wang},
title={Automatic Detection and Identification of Trichomonas Vaginalis from Fluorescence Microscopy Images},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING},
year={2022},
pages={190-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010993400003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING
TI - Automatic Detection and Identification of Trichomonas Vaginalis from Fluorescence Microscopy Images
SN - 978-989-758-552-4
AU - Yu Y.
AU - Wang J.
PY - 2022
SP - 190
EP - 197
DO - 10.5220/0010993400003123
PB - SciTePress