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Hybrid segmentation of left ventricle in cardiac MRI using gaussian-mixture model and region restricted dynamic programming.
MedLine Citation:
PMID:  23245907     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Segmentation of the left ventricle from cardiac magnetic resonance images (MRI) is very important to quantitatively analyze global and regional cardiac function. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic left ventricle segmentation on short-axis cardiac MRI. The database used in this study consists of three data sets obtained from the Sunnybrook Health Sciences Centre. Each data set contains 15 cases (4 ischemic heart failures, 4 non-ischemic heart failures, 4 left ventricle (LV) hypertrophies and 3 normal cases). Three key techniques are developed in this segmentation algorithm: (1) ray scanning approach is designed for segmentation of images with left ventricular outflow tract (LVOT), (2) a region restricted technique is employed for epicardial contour extraction, and (3) an edge map with non-maxima gradient suppression approach is put forward to improve the dynamic programming to derive the epicardial boundary. The validation experiments were performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 91%, the average perpendicular distance is about 2mm. The overlapping dice metric is about 0.92. The regression and determination coefficient between the experts and our proposed method on the ejection fraction (EF) is 1.01 and 0.9375, respectively; they are 0.9 and 0.8245 for LV mass. The proposed segmentation method shows the better performance and is very promising in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.
Authors:
Huaifei Hu; Haihua Liu; Zhiyong Gao; Lu Huang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-12-13
Journal Detail:
Title:  Magnetic resonance imaging     Volume:  -     ISSN:  1873-5894     ISO Abbreviation:  Magn Reson Imaging     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-12-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8214883     Medline TA:  Magn Reson Imaging     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012 Elsevier Inc. All rights reserved.
Affiliation:
College of Biomedical Engineering, South-Central University for Nationalities, Wuhan Hubei Province, P.R. China 430074.
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