Segmentation of the Left Ventricle Using Distance Regularized Two-Layer Level Set Approach

Abstract

We propose a novel two-layer level set approach for segmentation of the left ventricle (LV) from cardiac magnetic resonance (CMR) short-axis images. In our method, endocardium and epicardium are represented by two specified level contours of a level set function. Segmentation of the LV is formulated as a problem of optimizing the level set function such that these two level contours best fit the epicardium and endocardium. More importantly, a distance regularization (DR) constraint on the level contours is introduced to preserve smoothly varying distance between them. This DR constraint leads to a desirable interaction between the level contours that contributes to maintain the anatomical geometry of the endocardium and epicardium. The negative influence of intensity inhomogeneities on image segmentation are overcome by using a data term derived from a local intensity clustering property. Our method is quantitatively validated by experiments on the datasets for the MICCAI grand challenge on left ventricular segmentation, which demonstrates the advantages of our method in terms of segmentation accuracy and consistency with anatomical geometry.