Knowledge-guided left ventricular boundary detection

Interpolation of X-ray motion pictures of the heart (cineventiculograms of the left ventricle) is complicated by the low contrast of the images and the elastic motion of the heart. Here, the problem of placing the heart boundary in each frame of the motion sequence is described by a framework for the application of knowledge in the form of diagnostically relevant models of the heart in motion. A blackboard architecture is utilized as a basis for the image interpretation. In this architecture, local features such as edges are grouped to build a complete description of the moving heart; the knowledge is organized in a hierarchy with knowledge sources (KSs) operating on different levels of the hierarchy and opportunistic problem-solving techniques are used to control the order of activation of both data-directed and goal-driven sources