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access icon free Segmentation of the left ventricle in short-axis sequences by combining deformation flow and optical flow

© The Institution of Engineering and Technology
Received 12/06/2016, Accepted 11/12/2016, Revised 08/10/2016, Published 13/12/2016

To help the clinicians to segment the borders of the left ventricle (LV) efficiently during measurement of the heart, the authors come up with a semi-automatic approach in this study that is capable of identifying the endocardial borders robustly from cine magnetic resonance images. Firstly, the deformation flow is computed between the inputted boundary in the previous frame and the extracted edge of the LV in the current frame based on boundary minimum distance principle (BMDP). Then, the deformation flow is constrained by optical flow calculated by a partial differential equation model. A smooth deformation boundary is then formed by minimising the energy between the previously inputted boundary and the rough boundary obtained by BMDP and optical flow constraint. To extract edge of the LV as accurate as possible, a threshold selection method is used and improved based on the previous study. The proposed approach is tested on the open access dataset. The computed average perpendicular distance is 1.36 ± 0.24 mm and the computed Dice measure is 90.7% ± 0.15%. Experimental results show that the proposed approach is significantly more accurate than the referenced state of art methods.

Inspec keywords: brain; cardiology; biomedical MRI; image segmentation; edge detection; medical image processing

Other keywords: BMDP; endocardial borders; open access dataset; left ventricle segmentation; optical flow constraint; semiautomatic approach; boundary minimum distance principle; computed average perpendicular distance; cine magnetic resonance images; partial differential equation model; deformation flow; inputted boundary; smooth deformation boundary; heart measurement; short-axis sequences

Subjects: Biomedical magnetic resonance imaging and spectroscopy; Biology and medical computing; Computer vision and image processing techniques; Optical, image and video signal processing; Medical magnetic resonance imaging and spectroscopy; Patient diagnostic methods and instrumentation

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