DocumentCode :
1149040
Title :
Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI
Author :
Lee, Hae-Yeoun ; Codella, Noel C F ; Cham, Matthew D. ; Weinsaft, Jonathan W. ; Wang, Yi
Author_Institution :
Weill Med. Coll., Dept. of Radiol., Cornell Univ., Ithaca, NY, USA
Volume :
57
Issue :
4
fYear :
2010
fDate :
4/1/2010 12:00:00 AM
Firstpage :
905
Lastpage :
913
Abstract :
An automatic left ventricle (LV) segmentation algorithm is presented for quantification of cardiac output and myocardial mass in clinical practice. The LV endocardium is first segmented using region growth with iterative thresholding by detecting the effusion into the surrounding myocardium and tissues. Then the epicardium is extracted using the active contour model guided by the endocardial border and the myocardial signal information estimated by iterative thresholding. This iterative thresholding and active contour model with adaptation (ITHACA) algorithm was compared to manual tracing used in clinical practice and the commercial MASS Analysis software (General Electric) in 38 patients, with Institutional Review Board (IRB) approval. The ITHACA algorithm provided substantial improvement over the MASS software in defining myocardial borders. The ITHACA algorithm agreed well with manual tracing with a mean difference of blood volume and myocardial mass being 2.9 ?? 6.2 mL (mean ?? standard deviation) and -0.9 ?? 16.5 g, respectively. The difference was smaller than the difference between manual tracing and the MASS software (approximately -20.0 ?? 6.9 mL and -1.0 ?? 20.2 g, respectively). These experimental results support that the proposed ITHACA segmentation is accurate and useful for clinical practice.
Keywords :
biomedical MRI; cardiology; image segmentation; iterative methods; medical image processing; ITHACA algorithm; Iterative Thresholding and Active Contour Model with Adaptation; LV segmentation algorithm; active contour model; automatic left ventricle segmentation; cardiac output quantification; effusion detection; endocardial border; iterative thresholding; myocardial mass quantification; myocardial signal information; region growth; short axis cardiac MRI; Active contour model; cardiac MRI; cine MRI; iterative thresholding; left ventricle (LV) segmentation; Aged; Algorithms; Blood Volume; Cardiac Volume; Female; Heart; Heart Ventricles; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging, Cine; Male; Middle Aged; Models, Cardiovascular; Retrospective Studies;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2009.2014545
Filename :
4776520
Link To Document :
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