title: Left ventricle segmentation by dynamic shape constrained random walks.
abstract: 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.
authors.forename: Xulei
authors.initials: X
authors.lastname: Yang
authors.forename: Yi
authors.initials: Y
authors.lastname: Su
authors.forename: Min
authors.initials: M
authors.lastname: Wan
authors.forename: Si Yong
authors.initials: SY
authors.lastname: Yeo
authors.forename: Calvin
authors.initials: C
authors.lastname: Lim
authors.forename: Sum Thai
authors.initials: ST
authors.lastname: Wong
authors.forename: Liang
authors.initials: L
authors.lastname: Zhong
authors.forename: Ru San
authors.initials: RS
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results