Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0092382
Title: Automatic localization of the left ventricle from cardiac cine magnetic resonance imaging: A new spectrum-based computer-aided tool
Authors: Zhong L. 
Zhang J.-M. 
Zhao X.
Tan R.S. 
Wan M.
Keywords: adult
article
automation
cardiac imaging
cardiovascular magnetic resonance
computer assisted radiography
female
Fourier transformation
heart left ventricle
heart right ventricle
human
human experiment
image analysis
image display
image processing
male
middle aged
normal human
quantitative analysis
young adult
algorithm
anisotropy
automated pattern recognition
cardiology
computer assisted diagnosis
computer program
Fourier analysis
heart left ventricle function
heart ventricle
nuclear magnetic resonance imaging
pathology
procedures
theoretical model
Adult
Algorithms
Anisotropy
Automation
Cardiology
Diagnosis, Computer-Assisted
Female
Fourier Analysis
Healthy Volunteers
Heart Ventricles
Humans
Magnetic Resonance Imaging, Cine
Male
Middle Aged
Models, Theoretical
Pattern Recognition, Automated
Software
Ventricular Function, Left
Issue Date: 2014
Citation: Zhong L., Zhang J.-M., Zhao X., Tan R.S., Wan M. (2014). Automatic localization of the left ventricle from cardiac cine magnetic resonance imaging: A new spectrum-based computer-aided tool. PLoS ONE 9 (4) : e92382. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0092382
Abstract: Traditionally, cardiac image analysis is done manually. Automatic image processing can help with the repetitive tasks, and also deal with huge amounts of data, a task which would be humanly tedious. This study aims to develop a spectrum-based computer-aided tool to locate the left ventricle using images obtained via cardiac magnetic resonance imaging. Discrete Fourier Transform was conducted pixelwise on the image sequence. Harmonic images of all frequencies were analyzed visually and quantitatively to determine different patterns of the left and right ventricles on spectrum. The first and fifth harmonic images were selected to perform an anisotropic weighted circle Hough detection. This tool was then tested in ten volunteers. Our tool was able to locate the left ventricle in all cases and had a significantly higher cropping ratio of 0.165 than did earlier studies. In conclusion, a new spectrum-based computer aided tool has been proposed and developed for automatic left ventricle localization. The development of this technique, which will enable the automatic location and further segmentation of the left ventricle, will have a significant impact in research and in diagnostic settings. We envisage that this automated method could be used by radiographers and cardiologists to diagnose and assess ventricular function in patients with diverse heart diseases. © 2014 Zhong et al.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/161418
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0092382
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