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Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

BACKGROUND: Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ve...

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Autores principales: Melo, Salvador A, Macchiavello, Bruno, Andrade, Marcelino M, Carvalho, João LA, Carvalho, Hervaldo S, Vasconcelos, Daniel F, Berger, Pedro A, da Rocha, Adson F, Nascimento, Francisco AO
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224979/
https://www.ncbi.nlm.nih.gov/pubmed/20078864
http://dx.doi.org/10.1186/1475-925X-9-5
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author Melo, Salvador A
Macchiavello, Bruno
Andrade, Marcelino M
Carvalho, João LA
Carvalho, Hervaldo S
Vasconcelos, Daniel F
Berger, Pedro A
da Rocha, Adson F
Nascimento, Francisco AO
author_facet Melo, Salvador A
Macchiavello, Bruno
Andrade, Marcelino M
Carvalho, João LA
Carvalho, Hervaldo S
Vasconcelos, Daniel F
Berger, Pedro A
da Rocha, Adson F
Nascimento, Francisco AO
author_sort Melo, Salvador A
collection PubMed
description BACKGROUND: Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve. METHODS: This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles. RESULTS: The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated. CONCLUSIONS: The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.
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spelling pubmed-32249792011-11-29 Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle Melo, Salvador A Macchiavello, Bruno Andrade, Marcelino M Carvalho, João LA Carvalho, Hervaldo S Vasconcelos, Daniel F Berger, Pedro A da Rocha, Adson F Nascimento, Francisco AO Biomed Eng Online Research BACKGROUND: Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve. METHODS: This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles. RESULTS: The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated. CONCLUSIONS: The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle. BioMed Central 2010-01-15 /pmc/articles/PMC3224979/ /pubmed/20078864 http://dx.doi.org/10.1186/1475-925X-9-5 Text en Copyright ©2010 Melo et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Melo, Salvador A
Macchiavello, Bruno
Andrade, Marcelino M
Carvalho, João LA
Carvalho, Hervaldo S
Vasconcelos, Daniel F
Berger, Pedro A
da Rocha, Adson F
Nascimento, Francisco AO
Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle
title Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle
title_full Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle
title_fullStr Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle
title_full_unstemmed Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle
title_short Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle
title_sort semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224979/
https://www.ncbi.nlm.nih.gov/pubmed/20078864
http://dx.doi.org/10.1186/1475-925X-9-5
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