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Automatic Left Ventricle Segmentation in Cardiac MRI Using Topological Stable-State Thresholding and Region Restricted Dynamic Programming.
MedLine Citation:
PMID:  22465463     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
RATIONALE AND OBJECTIVES: Segmentation of the left ventricle (LV) is very important in the assessment of cardiac functional parameters. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic LV segmentation on short-axis cardiac magnetic resonance images (MRI). MATERIALS AND METHODS: The database used in this study consists of 45 cases obtained from the Sunnybrook Health Sciences Centre. The 45 cases contain 12 ischemic heart failures, 12 non-ischemic heart failures, 12 LV hypertrophies, and 9 normal cases. Three key techniques are developed in this segmentation algorithm: 1) topological stable-state thresholding method is proposed to refine the endocardial contour, 2) an edge map with non-maxima gradient suppression approach, and 3) a region-restricted technique that is proposed to improve the dynamic programming to derive the epicardial boundary. RESULTS: The validation experiments were performed on a pool of data sets of 45 cases. For both endo- and epicardial contours of our results, percentage of good contours is about 91%, the average perpendicular distance is about 2 mm, and the overlapping dice metric is about 0.91. The regression and determination coefficient for the experts and our proposed method on the ejection fraction is 1.05 and 0.9048, respectively; they are 0.98 and 0.8221 for LV mass. CONCLUSIONS: An automatic method using topological stable-state thresholding and region restricted dynamic programming has been proposed to segment left ventricle in short-axis cardiac MRI. Evaluation results indicate that the proposed segmentation method can improve the accuracy and robust of left ventricle segmentation. The proposed segmentation approach shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.
Authors:
Hong Liu; Huaifei Hu; Xiangyang Xu; Enmin Song
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-3-31
Journal Detail:
Title:  Academic radiology     Volume:  -     ISSN:  1878-4046     ISO Abbreviation:  -     Publication Date:  2012 Mar 
Date Detail:
Created Date:  2012-4-2     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9440159     Medline TA:  Acad Radiol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.
Affiliation:
Center for Biomedical Imaging and Bioinformatics, Key Laboratory of Education Ministry for Image Processing and Intelligence Control, School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luo Yu Road, Wuhan, Hubei, China.
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