Left Ventricle Segmentation Using Model Fitting and Active Surfaces

Authors:
Peter C. Tay;Bing Li;Chris D. Garson;Scott T. Acton;John A. Hossack
Affiliations:
Department of Electrical and Computer Engineering Technology, Western Carolina University, Cullowhee, USA 28723;Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, USA 22904;Department of Biomedical Engineering, University of Virginia, Charlottesville, USA 22908;Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, USA 22904 and Department of Biomedical Engineering, University of Virginia, Charlottesville, USA 22908;Department of Biomedical Engineering, University of Virginia, Charlottesville, USA 22908
Venue:
Journal of Signal Processing Systems
Year:
2009

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Abstract

A method to perform 4D (3D over time) seg mentation of the left ventricle of a mouse heart using a set of B mode cine slices acquired in vivo from a series of short axis scans is described. We incorporate previ ously suggested methods such as temporal propagation, the gradient vector flow active surface, superquadric models, etc. into our proposed 4D segmentation of the left ventricle. The contributions of this paper are incor poration of a novel despeckling method and the use of locally fitted superellipsoid models to provide a better initialization for the active surface segmentation algorithm. Average distances of the improved surface segmentation to a manually segmented surface through out the entire cardiac cycle and cross-sectional contours are provided to demonstrate the improvements pro duced by the proposed 4D segmentation.