Active contour model and nonlinear shape priors with application to left ventricle segmentation in cardiac MR images

摘要

This paper presents a new active contour model for left ventricle segmentation in cardiac magnetic resonance (MR) images. In this study, the shape prior is coupled with intensity information in the proposed energy functional which includes a data term and a shape term. The data term, inspired from a region-based active contour model, is used to guide the motion of the initial curve to desired object boundaries. Meanwhile, the shape term is utilized to constrain the evolving contour with respect to the reference shape, which helps the model deal with images in the presence of background clutter and object occlusion. Especially, in the paper, to reconstruct the shape prior, we utilize the kernel principal component analysis (KPCA) that allows the obtained prior shape to be faithful to the shape of the desired object. In addition, the pose variations between the shapes are handled by employing shape normalization procedure instead of solving a set of Euler-Lagrange equations as in conventional approaches. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase formulation. Comparative experiments on a set of cardiac MR images show the advantages of the proposed model. (C) 2015 Elsevier GmbH. All rights reserved.