title: Semiautomatic segmentation of aortic valve from sequenced ultrasound image using a novel shape-constraint GCV model.
abstract: OBJECTIVE
  Effective and accurate segmentation of the aortic valve (AV) from sequenced ultrasound (US) images remains a technical challenge because of intrinsic factors of ultrasound images that impact the quality and the continuous changes of shape and position of segmented objects. In this paper, a novel shape-constraint gradient Chan-Vese (GCV) model is proposed for segmenting the AV from time serial echocardiography.
  
  
  METHODS
  The GCV model is derived by incorporating the energy of the gradient vector flow into a CV model framework, where the gradient vector energy term is introduced by calculating the deviation angle between the inward normal force of the evolution contour and the gradient vector force. The flow force enlarges the capture range and enhances the blurred boundaries of objects. This is achieved by adding a circle-like contour (constructed using the AV structure region as a constraint shape) as an energy item to the GCV model through the shape comparison function. This shape-constrained energy can enhance the image constraint force by effectively connecting separate gaps of the object edge as well as driving the evolution contour to quickly approach the ideal object. Because of the slight movement of the AV in adjacent frames, the initial constraint shape is defined by users, with the other constraint shapes being derived from the segmentation results of adjacent sequence frames after morphological filtering. The AV is segmented from the US images by minimizing the proposed energy function.
  
  
  RESULTS
  To evaluate the performance of the proposed method, five assessment parameters were used to compare it with manual delineations performed by radiologists (gold standards). Three hundred and fifteen images acquired from nine groups were analyzed in the experiment. The area-metric overlap error rate was 6.89% ± 2.88%, the relative area difference rate 3.94% ± 2.63%, the average symmetric contour distance 1.08 ± 0.43 mm, the root mean square symmetric contour distance 1.37 ± 0.52 mm, and the maximum symmetric contour distance was 3.57 ± 1.72 mm.
  
  
  CONCLUSIONS
  Compared with the CV model, as a result of the combination of the gradient vector and neighborhood shape information, this semiautomatic segmentation method significantly improves the accuracy and robustness of AV segmentation, making it feasible for improved segmentation of aortic valves from US images that have fuzzy boundaries.
authors.affiliation: Multi-disciplinary Research Center, Hebei University, Baoding 071000, China.
authors.forename: Yiting
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Hebei University Affiliated Hospital, Hebei Baoding 071000, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Dong
authors.affiliation: College of Mathematics and Computer Science, Hebei University, Baoding 071000, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Wang
authors.affiliation: Department of Cardiovascular, Peking Union Medical College Hospital, Beijing 100005, China.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Xie
authors.affiliation: Department of Cardiovascular, Peking Union Medical College Hospital, Beijing 100005, China.
authors.forename: Shuyang
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Multi-disciplinary Research Center, Hebei University, Baoding 071000, China and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
authors.forename: Lixu
authors.initials: L
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D001021:Aortic Valve / Q000000981:diagnostic imaging*; D004452:Echocardiography / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*