A new framework for automated identification of pathological tissues in contrast enhanced cardiac magnetic resonance images

A novel automated framework for quantification of myocardial viability in contrast enhanced cardiac magnetic resonance images (CE-CMRI) is proposed. The framework consists of three main steps. First, the inner and outer borders of the left ventricle (LV) wall (myocardium wall) are segmented from the surrounding tissue. Second, the pathological tissue in the myocardium wall is identified using a MAP-based classifier based on the visual appearance and spatial interaction of the LV pathological tissue as well as healthy tissue. Third, the myocardial viability is assessed and quantified based on measuring two parameters: the percentage of pathological tissue with respect to the area of the myocardium wall and the transmural extent of the pathological tissue in the myocardium wall. The transmural extent is estimated based on a new Partial Differential Equation (PDE) approach to determine point-to-point correspondences between the inner and outer borders of the pathological area as well as the myocardium wall. The proposed framework was tested on in-vivo CE-CMR images and validated with manual expert delineations of pathological tissue. Experiments and comparison results on real CE-CMR images confirm the robustness and accuracy of the proposed framework over the existing ones.