Paper Info
Title
Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing.
Abstract
Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this paper, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardium and then segmenting the epicardium. To this end, the endocardium is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result. The robustness and accuracy of the proposed approach is demonstrated by experimental results from 33 human and 12 pig CT images.
Year
DOI
Venue
2013
10.1109/TBME.2013.2266118
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
cardiac disease prognosis,right ventricle segmentation,image quality,active contour model,computerised tomography,myocardial wall extraction,variational region-growing model,left ventricle (lv),variational region growing,right ventricle (rv),image segmentation,computed tomography,shape segmentation,heart surface extraction,endocardium localization,cardiac disease diagnosis,epicardium segementation,cardiovascular system,physiological models,salient component,feature extraction,geometric feature utilization,image sequences,cardiac ct image segmentation,myocardial wall segmentation,myocardial wall delineation,principal component analysis,left ventricle segmentation,blood pool surface,medical image processing,blood,endocardium,algorithms
Active contour model,Computer vision,Ejection fraction,Segmentation,Computer science,Image quality,Image segmentation,Artificial intelligence,Ventricle,Region growing,Endocardium
Journal
Volume
Issue
ISSN
60
10
1558-2531
Citations 
PageRank 
References 
0
0.34
17
Authors
8
Name
Order
Citations
PageRank
Liangjia Zhu1929.07
Yi Gao211518.29
Vikram Appia3242.73
Anthony J. Yezzi42016151.48
Chesnal Arepalli572.06
Tracy L. Faber672.13
Arthur Stillman741.04
Allen Tannenbaum83629409.15