29 March 2016 An efficient method for accurate segmentation of LV in contrast-enhanced cardiac MR images
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Abstract
Segmentation of left ventricle (LV) in contrast-enhanced cardiac MR images is a challenging task because of high variability in the image intensity. This is due to a) wash-in and wash-out of the contrast agent over time and b) poor contrast around the epicardium (outer wall) region. Current approaches for segmentation of the endocardium (inner wall) usually involve application of a threshold within the region of interest, followed by refinement techniques like active contours. A limitation of this method is under-segmentation of the inner wall because of gradual loss of contrast at the wall boundary. On the other hand, the challenge in outer wall segmentation is the lack of reliable boundaries because of poor contrast. There are four main contributions in this paper to address the aforementioned issues. First, a seed image is selected using variance based approach on 4D time-frame images over which initial endocardium and epicardium is segmented. Secondly, we propose a patch based feature which overcomes the problem of gradual contrast loss for LV endocardium segmentation. Third, we propose a novel Iterative-Edge-Refinement (IER) technique for epicardium segmentation. Fourth, we propose a greedy search algorithm for propagating the initial contour segmented on seed-image across other time frame images. We have experimented our technique on five contrast-enhanced cardiac MR Datasets (4D) having a total of 1097 images. The segmentation results for all 1097 images have been visually inspected by a clinical expert and have shown good accuracy.
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Venkata Suryanarayana K., Venkata Suryanarayana K., Abhishek Mitra, Abhishek Mitra, Srikrishnan V., Srikrishnan V., Hyun Hee Jo, Hyun Hee Jo, Anup Bidesi, Anup Bidesi, "An efficient method for accurate segmentation of LV in contrast-enhanced cardiac MR images", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97882E (29 March 2016); doi: 10.1117/12.2216704; https://doi.org/10.1117/12.2216704
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