IEEE Transactions on Pattern Analysis and Machine Intelligence
Wall Motion Classification of Stress Echocardiography Based on Combined Rest-and-Stress Data
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
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Automating the analysis of left ventricular (LV) wall motion can improve objective prediction of coronary artery disease. A new method for classifying LV wall motion using shape models with localized variations was developed for this purpose. These sparse shape models were built from four-chamber and two-chamber echocardiographic sequences using principal component analysis and orthomax rotations. The resulting shape parameters were then used to classify wall-motion abnormalities of LV segments. Compared with the shape model before rotation, higher classification correctness was achieved using significantly less shape parameters. The local variations exhibited by these shape parameters correlated reasonably with the location of the segments