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Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos
In this work, a novel algorithmic scheme is developed that processes echocardiogram videos, and tracks the movement of the mitral valve leaflets, and thereby estimates whether the movement is symptomatic of a healthy or diseased heart. This algorithm uses automatic Otsu’s thresholding to find a clos...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321051/ https://www.ncbi.nlm.nih.gov/pubmed/34460750 http://dx.doi.org/10.3390/jimaging6090093 |
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author | Shahid, Kazi Tanzeem Schizas, Ioannis |
author_facet | Shahid, Kazi Tanzeem Schizas, Ioannis |
author_sort | Shahid, Kazi Tanzeem |
collection | PubMed |
description | In this work, a novel algorithmic scheme is developed that processes echocardiogram videos, and tracks the movement of the mitral valve leaflets, and thereby estimates whether the movement is symptomatic of a healthy or diseased heart. This algorithm uses automatic Otsu’s thresholding to find a closed boundary around the left atrium, with the basic presumption that it is situated in the bottom right corner of the apical 4 chamber view. A centroid is calculated, and protruding prongs are taken within a 40-degree cone above the centroid, where the mitral valve is located. Binary images are obtained from the videos where the mitral valve leaflets have different pixel values than the cavity of the left atrium. Thus, the points where the prongs touch the valve will show where the mitral valve leaflets are located. The standard deviation of these points is used to calculate closeness of the leaflets. The estimation of the valve movement across subsequent frames is used to determine if the movement is regular, or affected by heart disease. Tests conducted with numerous videos containing both healthy and diseased hearts attest to our method’s efficacy, with a key novelty in being fully unsupervised and computationally efficient. |
format | Online Article Text |
id | pubmed-8321051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83210512021-08-26 Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos Shahid, Kazi Tanzeem Schizas, Ioannis J Imaging Article In this work, a novel algorithmic scheme is developed that processes echocardiogram videos, and tracks the movement of the mitral valve leaflets, and thereby estimates whether the movement is symptomatic of a healthy or diseased heart. This algorithm uses automatic Otsu’s thresholding to find a closed boundary around the left atrium, with the basic presumption that it is situated in the bottom right corner of the apical 4 chamber view. A centroid is calculated, and protruding prongs are taken within a 40-degree cone above the centroid, where the mitral valve is located. Binary images are obtained from the videos where the mitral valve leaflets have different pixel values than the cavity of the left atrium. Thus, the points where the prongs touch the valve will show where the mitral valve leaflets are located. The standard deviation of these points is used to calculate closeness of the leaflets. The estimation of the valve movement across subsequent frames is used to determine if the movement is regular, or affected by heart disease. Tests conducted with numerous videos containing both healthy and diseased hearts attest to our method’s efficacy, with a key novelty in being fully unsupervised and computationally efficient. MDPI 2020-09-09 /pmc/articles/PMC8321051/ /pubmed/34460750 http://dx.doi.org/10.3390/jimaging6090093 Text en © 2020 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Shahid, Kazi Tanzeem Schizas, Ioannis Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos |
title | Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos |
title_full | Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos |
title_fullStr | Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos |
title_full_unstemmed | Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos |
title_short | Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos |
title_sort | unsupervised mitral valve tracking for disease detection in echocardiogram videos |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321051/ https://www.ncbi.nlm.nih.gov/pubmed/34460750 http://dx.doi.org/10.3390/jimaging6090093 |
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