Cardiac MRI steam images denoising using Bayesian classifier

A.G. Motaal; M.A. AlAttar; N.F. Osman; A.S. Fahmy

doi:10.1109/CIBEC.2008.4786056

Cardiac MRI steam images denoising using Bayesian classifier

A.G. Motaal, M.A. AlAttar, N.F. Osman, A.S. Fahmy

Medical Image Analysis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

Imaging of the heart anatomy and function using magnetic resonance imaging (MRI) is an important diagnosis tool for heart diseases. Several techniques have been developed to increase the contrast-to-noise ratio (CNR) between myocardium and background. Recently, a technique that acquires cine cardiac images with black-blood contrast has been proposed. Although the technique produces cine sequence of high contrast, it suffers from elevated noise which limits the CNR. In this paper, we study the performance and efficiency of applying a Bayes classifier to remove background noise. Real MRI data is used to test and validate the proposed method; In addition, a quantitative comparison is done between the proposed method and other thresholding-based classifications techniques.

Original language	English
Title of host publication	Proceedings of the Biomedical Engineering Conference (CIBEC 2008, 18-20 December 2008, Cairo, Egypt
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-4
ISBN (Print)	978-1-4244-2694-2
DOIs	https://doi.org/10.1109/CIBEC.2008.4786056
Publication status	Published - 2008

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title = "Cardiac MRI steam images denoising using Bayesian classifier",

abstract = "Imaging of the heart anatomy and function using magnetic resonance imaging (MRI) is an important diagnosis tool for heart diseases. Several techniques have been developed to increase the contrast-to-noise ratio (CNR) between myocardium and background. Recently, a technique that acquires cine cardiac images with black-blood contrast has been proposed. Although the technique produces cine sequence of high contrast, it suffers from elevated noise which limits the CNR. In this paper, we study the performance and efficiency of applying a Bayes classifier to remove background noise. Real MRI data is used to test and validate the proposed method; In addition, a quantitative comparison is done between the proposed method and other thresholding-based classifications techniques.",

author = "A.G. Motaal and M.A. AlAttar and N.F. Osman and A.S. Fahmy",

year = "2008",

doi = "10.1109/CIBEC.2008.4786056",

language = "English",

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Cardiac MRI steam images denoising using Bayesian classifier. / Motaal, A.G.; AlAttar, M.A.; Osman, N.F. et al.
Proceedings of the Biomedical Engineering Conference (CIBEC 2008, 18-20 December 2008, Cairo, Egypt. Piscataway: Institute of Electrical and Electronics Engineers, 2008. p. 1-4.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Cardiac MRI steam images denoising using Bayesian classifier

AU - Motaal, A.G.

AU - AlAttar, M.A.

AU - Osman, N.F.

AU - Fahmy, A.S.

PY - 2008

Y1 - 2008

N2 - Imaging of the heart anatomy and function using magnetic resonance imaging (MRI) is an important diagnosis tool for heart diseases. Several techniques have been developed to increase the contrast-to-noise ratio (CNR) between myocardium and background. Recently, a technique that acquires cine cardiac images with black-blood contrast has been proposed. Although the technique produces cine sequence of high contrast, it suffers from elevated noise which limits the CNR. In this paper, we study the performance and efficiency of applying a Bayes classifier to remove background noise. Real MRI data is used to test and validate the proposed method; In addition, a quantitative comparison is done between the proposed method and other thresholding-based classifications techniques.

AB - Imaging of the heart anatomy and function using magnetic resonance imaging (MRI) is an important diagnosis tool for heart diseases. Several techniques have been developed to increase the contrast-to-noise ratio (CNR) between myocardium and background. Recently, a technique that acquires cine cardiac images with black-blood contrast has been proposed. Although the technique produces cine sequence of high contrast, it suffers from elevated noise which limits the CNR. In this paper, we study the performance and efficiency of applying a Bayes classifier to remove background noise. Real MRI data is used to test and validate the proposed method; In addition, a quantitative comparison is done between the proposed method and other thresholding-based classifications techniques.

U2 - 10.1109/CIBEC.2008.4786056

DO - 10.1109/CIBEC.2008.4786056

M3 - Conference contribution

SN - 978-1-4244-2694-2

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BT - Proceedings of the Biomedical Engineering Conference (CIBEC 2008, 18-20 December 2008, Cairo, Egypt

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Cardiac MRI steam images denoising using Bayesian classifier

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