Segmentation of left ventricle in cardiac MRI images using adaptive multi-seeded region growing

Abstract

Multi-slice short-axis acquisitions of the left ventricle are fundamental for estimating the volume and mass of the left ventricle in cardiac MRI scans. Manual segmentation of the myocardium in all time frames per each cross-section is a cumbersome task. Therefore, automatic myocardium segmentation methods are essential for cardiac functional analysis. Region growing has been proposed to segment the myocardium. Although the technique is simple and fast, non uniform intensity and low-contrast interfaces of the myocardium are major challenges of the technique that limit its use in myocardial segmentation. In this work, we propose a modified region growing technique that ensures reliable and fast myocardial segmentation of short-axis images. The proposed technique initializes the region growing process using different seed points chosen automatically according to the information of the mid-contour profile. Then two types of spatial constraints are used to guarantee fast and accurate segmentation. The technique has been tested and validated quantitatively using a large number of images of different qualities. The results confirm the reliability and accuracy of the proposed technique.