A system for knowledge-based boundary detection of cardiac magnetic resonance image sequences

A knowledge-based system is described for boundary detection from magnetic resonance image sequences of a beating heart. It is shown that the Dempster/Shafer theory and fuzzy set theory can be used for control of the system as well as for labeling objects in the images. The quality of the images is measured for ordering the images in boundary detection. The performance of the various knowledge sources is evaluated for learning from experience. These approaches provide the system with top-down feedback functions that can be utilized for more efficient use of limited computational resources. The basic structure of the system and the function of each component is described. The performance of the components is presented with some examples.<
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