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15 May 2003 Automated classification of wall motion abnormalities by principal component analysis of endocardial shape motion patterns in echocardiograms
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Abstract
Principal Component Analysis of sets of temporal shape sequences renders eigenvariations of shape/motion, including typical normal and pathological endocardial contraction patterns. A previously developed Active Appearance Model for time sequences (AAMM) was employed to derive AAMM shape coefficients (ASCs) and we hypothesized these would allow classification of wall motion abnormalities (WMA). A set of stress echocardiograms (single-beat 4-chamber and 2-chamber sequences with expert-verified endocardial contours) of 129 infarct patients was split randomly into training (n=65) and testing (n=64) sets. AAMMs were generated from the training set and for all sequences ASCs were extracted and statistically related to regional/global Visual Wall Motion Scoring (VWMS) and clinical infarct severity and volumetric parameters. Linear regression showed clear correlations between ASCs and VWMS. Infarct severity measures correlated poorly to both ASCs and VWMS. Discriminant analysis showed good prediction from low #ASCs of both segmental (85% correctness) and global WMA (90% correctness). Volumetric parameters correlated poorly to regional VWMS. Conclusions: 1)ASCs show promising accuracy for automated WMA classification. 2)VWMS and endocardial border motion are closely related; with accurate automated border detection, automated WMA classification should be feasible. 3)ASC shape analysis allows contour set evaluation by direct comparison to clinical parameters.
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Johan G. Bosch, Francisca Nijland, Steven C. Mitchell, Boudewijn P. F. Lelieveldt, Otto Kamp, Milan Sonka, and Johan H. C. Reiber "Automated classification of wall motion abnormalities by principal component analysis of endocardial shape motion patterns in echocardiograms", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481135
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