Robust segmentation of human cardiac contours from spatial magnetic resonance images

Automated segmentation to find the endocardial boundary of the left heart ventricle from magnetic resonance (MR) images has shown to be a difficult task. One of the major problems related to the detection of the boundary are the shortcomings typical of discrete data, such as sampling artifacts and noise, which may cause the shape boundaries to be indistinct and disconnected. Furthermore, the structures inside the ventricular cavities, such as papillary muscles, are often indistinguishable from structures of interest for diagnostic analysis, such as the moving inner heart boundary. Thus, segmentation is error-prone and often incomplete. The aim of this work is to develop a model towards an automatic segmentation of the endocardial border. The proposed method is composed of two phases: The segmentation phase uses a bottom-up multi-scale analysis, based mainly on morphological scale-space processing by decomposing the image into a number of scales of different structure size. As a result of the decomposition, the structures adjacent to the endocardial border are located, and finally an estimated boundary is obtained regardless of those structures. The refinement phase subsequently asserts prior information about local structure around defined points along the shape boundary in order to obtain the best accuracy of the endocardial segmentation