Knowledge-based Registration & Segmentation of the Left Ventricle: A Level Set Approach

In this paper, we propose a level set formulation to deal with the segmentation and registration of the left ventricle in Magnetic Resonance (MR) images. Our approach is based on the integration of visual information, anatomical constraints and a flexible shape-driven cardiac model. The visual information is expressed through an intensity-based grouping module. The anatomical constraint accounts for the relative positions of the structures of interest. Global shape consistency is introduced by seeking for the lowest potential of the distance between the solution and the prior model. Registration is obtained using the same criterion where the transformation that aligns the latest segmentation map to either the shape model or to the previous segmentation result (temporal domain) is to be recovered.