A new framework for automated segmentation of left ventricle wall from contrast enhanced cardiac magnetic resonance images

A novel automated framework for the segmentation of the left ventricle (LV) wall from contrast enhanced cardiac magnetic resonance images (CE-CMRI) is proposed. The framework consists of two main steps. First, the inner cavity of the LV is segmented from the surrounding tissues based on finding the Maximum A Posteriori (MAP) estimation of a new energy function using a graph-cuts-based optimization algorithm. The proposed energy function consists of three descriptors: 1 st -order visual appearance descriptors of the CE-CMRI, a 2D spatially rotation-variant 2 nd -order homogeneity descriptor, and a LV inner cavity shape descriptor. Second, the outer contour of the LV is segmented by generating an orthogonal wave, starting from the LV inner contour, by solving an Eikonal partial differential equation with a new speed function that combines the prior shape and current visual appearance models of the LV wall. The proposed framework was tested on in-vivo CE-CMR images and validated with manual expert delineations of left ventricle borders. Experiments and comparison results on real CE-CMR images confirm the robustness and accuracy of the proposed framework over the existing ones.