RT Journal Article
A1 Ahmed O. Al-Agamy
AD Center for Informatics Science, Nile University, Cairo, Egypt
AD Electrical Engineering Department, Technical University of Eindhoven, Noord-Brabant, The Netherlands
A1 Nael F. Osman
AD Center for Informatics Science, Nile University, Cairo, Egypt
AD Radiology Departments, School of Medicine, Johns Hopkins University, Maryland, USA
A1 Ahmed S. Fahmy
AD Center for Informatics Science, Nile University, Cairo, Egypt
AD Systems and Biomedical Engineering Department, Cairo University, Cairo, Egypt

PB iet
T1 Myocardium segmentation in Strain-Encoded (SENC) magnetic resonance images using graph-cuts
JN IET Image Processing
VO 7
IS 5
SP 415
OP 422
AB Evaluation of cardiac functions using Strain Encoded (SENC) magnetic resonance (MR) imaging is a powerful tool for imaging the deformation of left and right ventricles. However, automated analysis of SENC images is hindered due to the low signal-to-noise ratio SENC images. In this work, the authors propose a method to segment the left and right ventricles myocardium simultaneously in SENC-MR short-axis images. In addition, myocardium seed points are automatically selected using skeletonisation algorithm and used as hard constraints for the graph-cut optimization algorithm. The method is based on a modified formulation of the graph-cuts energy term. In the new formulation, a signal probabilistic model is used, rather than the image histogram, to capture the characteristics of the blood and tissue signals and include it in the cost function of the graph-cuts algorithm. The method is applied to SENC datasets for 11 human subjects (five normal and six patients with known myocardial wall motion abnormality). The segmentation results of the proposed method are compared with those resulting from both manual segmentation and the conventional histogram-based graph-cuts segmentation algorithm. The results show that the proposed method outperforms the histogram-based graph-cuts algorithm especially to segment the thin structure of the right ventricle.
K1 graph-cut optimisation algorithm
K1 signal probabilistic model
K1 myocardium segmentation
K1 myocardial wall motion abnormality
K1 heart function
K1 SENC
K1 MRI
K1 left ventricles
K1 signal-to-noise ratio
K1 skeletonisation algorithm
K1 right ventricles
K1 strain-encoded magnetic resonance images
K1 myocardium borders
DO https://doi.org/10.1049/iet-ipr.2012.0513
UL https://digital-library.theiet.org/;jsessionid=1qlrhj0e9srf.x-iet-live-01content/journals/10.1049/iet-ipr.2012.0513
LA English
SN 1751-9659
YR 2013
OL EN