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Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis
Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6629065/ https://www.ncbi.nlm.nih.gov/pubmed/31306434 http://dx.doi.org/10.1371/journal.pone.0218861 |
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author | Díaz-Huerta, Jorge Luis Téllez-Anguiano, Adriana del Carmen Fraga-Aguilar, Miguelangel Gutiérrez-Gnecchi, José Antonio Arellano-Calderón, Sergio |
author_facet | Díaz-Huerta, Jorge Luis Téllez-Anguiano, Adriana del Carmen Fraga-Aguilar, Miguelangel Gutiérrez-Gnecchi, José Antonio Arellano-Calderón, Sergio |
author_sort | Díaz-Huerta, Jorge Luis |
collection | PubMed |
description | Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges. In this paper, a simple method that combines well-known segmentation algorithms is presented. This method is applied to detect acid-fast bacilli (AFB) in bacilloscopies used to diagnose pulmonary tuberculosis (TB). This diagnosis can be performed through different tests, and the most used worldwide is smear microscopy because of its low cost and effectiveness. This diagnosis technique is based on the analysis and counting of the bacilli in the bacilloscopy observed under an optical microscope. The proposed method is used to segment the bacilli in digital images from bacilloscopies processed using Ziehl-Neelsen (ZN) staining. The proposed method is fast, has a low computational cost and good efficiency compared to other methods. The bacilli image segmentation is performed by image processing and analysis techniques, probability concepts and classifiers. In this work, a Bayesian classifier based on a Gaussian mixture model (GMM) is used. The segmentations' results are validated by using the Jaccard index, which indicates the efficiency of the classifier. |
format | Online Article Text |
id | pubmed-6629065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-66290652019-07-25 Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis Díaz-Huerta, Jorge Luis Téllez-Anguiano, Adriana del Carmen Fraga-Aguilar, Miguelangel Gutiérrez-Gnecchi, José Antonio Arellano-Calderón, Sergio PLoS One Research Article Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges. In this paper, a simple method that combines well-known segmentation algorithms is presented. This method is applied to detect acid-fast bacilli (AFB) in bacilloscopies used to diagnose pulmonary tuberculosis (TB). This diagnosis can be performed through different tests, and the most used worldwide is smear microscopy because of its low cost and effectiveness. This diagnosis technique is based on the analysis and counting of the bacilli in the bacilloscopy observed under an optical microscope. The proposed method is used to segment the bacilli in digital images from bacilloscopies processed using Ziehl-Neelsen (ZN) staining. The proposed method is fast, has a low computational cost and good efficiency compared to other methods. The bacilli image segmentation is performed by image processing and analysis techniques, probability concepts and classifiers. In this work, a Bayesian classifier based on a Gaussian mixture model (GMM) is used. The segmentations' results are validated by using the Jaccard index, which indicates the efficiency of the classifier. Public Library of Science 2019-07-15 /pmc/articles/PMC6629065/ /pubmed/31306434 http://dx.doi.org/10.1371/journal.pone.0218861 Text en © 2019 Díaz-Huerta et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Díaz-Huerta, Jorge Luis Téllez-Anguiano, Adriana del Carmen Fraga-Aguilar, Miguelangel Gutiérrez-Gnecchi, José Antonio Arellano-Calderón, Sergio Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis |
title | Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis |
title_full | Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis |
title_fullStr | Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis |
title_full_unstemmed | Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis |
title_short | Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis |
title_sort | image processing for afb segmentation in bacilloscopies of pulmonary tuberculosis diagnosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6629065/ https://www.ncbi.nlm.nih.gov/pubmed/31306434 http://dx.doi.org/10.1371/journal.pone.0218861 |
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