Left ventricle segmentation by dynamic shape constrained random walks

Accurate and robust extraction of the left ventricle (LV) cavity is a key step for quantitative analysis of cardiac functions. In this study, we propose an improved LV cavity segmentation method that incorporates a dynamic shape constraint into the weighting function of the random walks algorithm. T...

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Bibliographic Details
Main Authors: Yang, Xulei, Su, Yi, Wan, Min, Yeo, Si Yong, Lim, Calvin, Wong, Sum Thai, Zhong, Liang, Tan, Ru San
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Cavity resonators
Heuristic algorithms
Image edge detection
Image segmentation
Shape
Online Access:Request full text
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Description
Summary:Accurate and robust extraction of the left ventricle (LV) cavity is a key step for quantitative analysis of cardiac functions. In this study, we propose an improved LV cavity segmentation method that incorporates a dynamic shape constraint into the weighting function of the random walks algorithm. The method involves an iterative process that updates an intermediate result to the desired solution. The shape constraint restricts the solution space of the segmentation result, such that the robustness of the algorithm is increased to handle misleading information that emanates from noise, weak boundaries, and clutter. Our experiments on real cardiac magnetic resonance images demonstrate that the proposed method obtains better segmentation performance than standard method.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2014.6944679