Description of the cardiac movement using hexagonal image structures

He, X; Jia, W; Wu, Q; Hintz, T

Description of the cardiac movement using hexagonal image structures

He, X

Jia, W

Wu, Q

Hintz, T

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Publication Type:: Journal Article
Citation:: Computerized Medical Imaging and Graphics, 2006, 30 (6-7), pp. 377 - 382
Issue Date:: 2006-09-01

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Field	Value	Language
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Hintz, T	en_US
dc.date.issued	2006-09-01	en_US
dc.identifier.citation	Computerized Medical Imaging and Graphics, 2006, 30 (6-7), pp. 377 - 382	en_US
dc.identifier.issn	0895-6111	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4432
dc.description.abstract	The most notable characteristic of the heart is its movement. Detection of dynamic information describing cardiac movement such as amplitude, speed and acceleration facilitates interpretation of normal and abnormal function. In recent years, the Omni-directional M-mode Echocardiography System (OMES) has been developed as a process that builds moving information from a sequence of echocardiography image frames. OMES detects cardiac movement through construction and analysis of Position-Time Grey Waveform (PTGW) images on some feature points of the boundaries of the ventricles. Image edge detection plays an important role in determining the feature boundary points and their moving directions as the basis for extraction of PTGW images-Spiral Architecture (SA) has proved efficient for image edge detection. SA is a hexagonal image structure in which an image is represented as a collection of hexagonal pixels. There are two operations called spiral addition and spiral multiplication defined on SA. They correspond to image translation and rotation, respectively. In this paper, we perform ventricle boundary detection based on SA using various defined chain codes. The gradient direction of each boundary point is determined at the same time. PTGW images at each boundary point are obtained through a series of spiral additions according to the directions of boundary points. Unlike the OMES system, our new approach is no longer affected by the translation movement of the heart. As its result, three curves representing the amplitude, speed and acceleration of cardiac movement can be easily drawn from the PTGW images obtained. Our approach is more efficient and accurate than OMES, and our results contain a more robust and complete description of cardiac motion. © 2006 Elsevier Ltd. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0451666
dc.relation.ispartof	Computerized Medical Imaging and Graphics	en_US
dc.relation.isbasedon	10.1016/j.compmedimag.2006.09.001	en_US
dc.subject.classification	Nuclear Medicine & Medical Imaging	en_US
dc.subject.mesh	Heart	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Image Enhancement	en_US
dc.subject.mesh	Subtraction Technique	en_US
dc.subject.mesh	Echocardiography	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Myocardial Contraction	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Information Storage and Retrieval	en_US
dc.title	Description of the cardiac movement using hexagonal image structures	en_US
dc.type	Journal Article
utslib.citation.volume	6-7	en_US
utslib.citation.volume	30	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	1103 Clinical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	6-7	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

The most notable characteristic of the heart is its movement. Detection of dynamic information describing cardiac movement such as amplitude, speed and acceleration facilitates interpretation of normal and abnormal function. In recent years, the Omni-directional M-mode Echocardiography System (OMES) has been developed as a process that builds moving information from a sequence of echocardiography image frames. OMES detects cardiac movement through construction and analysis of Position-Time Grey Waveform (PTGW) images on some feature points of the boundaries of the ventricles. Image edge detection plays an important role in determining the feature boundary points and their moving directions as the basis for extraction of PTGW images-Spiral Architecture (SA) has proved efficient for image edge detection. SA is a hexagonal image structure in which an image is represented as a collection of hexagonal pixels. There are two operations called spiral addition and spiral multiplication defined on SA. They correspond to image translation and rotation, respectively. In this paper, we perform ventricle boundary detection based on SA using various defined chain codes. The gradient direction of each boundary point is determined at the same time. PTGW images at each boundary point are obtained through a series of spiral additions according to the directions of boundary points. Unlike the OMES system, our new approach is no longer affected by the translation movement of the heart. As its result, three curves representing the amplitude, speed and acceleration of cardiac movement can be easily drawn from the PTGW images obtained. Our approach is more efficient and accurate than OMES, and our results contain a more robust and complete description of cardiac motion. © 2006 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/4432