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Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming

文献类型：期刊论文

作者G. Dharanibai2; Anupama Chandrasekharan1; Zachariah C. Alex2
刊名International Journal of Automation and Computing
出版日期2018
卷号15期号:6页码:673-688
关键词Cardiovascular magnetic resonance left ventricle endocardium epicardium myocardium segmentation active contour dynamic programming.
ISSN号1476-8186
DOI10.1007/s11633-018-1112-4
英文摘要The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.
源URL[http://ir.ia.ac.cn/handle/173211/42443]  
专题自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位1.Department of Radiology, Sri Ramachandra University, Chennai 600116, Tamil Nadu, India
2.School of Electronics Engineering, Vellore Institute of Technology (VIT) University, Vellore 632014, Tamil Nadu, India
推荐引用方式
GB/T 7714		 G. Dharanibai,Anupama Chandrasekharan,Zachariah C. Alex. Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming[J]. International Journal of Automation and Computing,2018,15(6):673-688.
APA G. Dharanibai,Anupama Chandrasekharan,&Zachariah C. Alex.(2018).Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming.International Journal of Automation and Computing,15(6),673-688.
MLA G. Dharanibai,et al."Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming".International Journal of Automation and Computing 15.6(2018):673-688.

入库方式： OAI收割

来源：自动化研究所

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