Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Machine Intell. 23(6), 681–685 (2001)
Stegmann, M.B.: Generative interpretation of medical images. PhD thesis, Technical University of Denmark (2004)
Bosch, J.G., Nijland, F., Mitchell, S.C., Lelieveldt, B.P.F., Kamp, O., Reiber, J.H.C., Sonka, M.: Computer-aided diagnosis via model-based shape analysis: Automated classification of wall motion abnormalities in echocardiograms. Acad. Radiol. 12(3), 358–367 (2005)
Üzümcü, M., Frangi, A.F., Reiber, J.H.C., Lelieveldt, B.P.F.: Independent component analysis in statistical shape models. In: Proc. SPIE Med. Imag.: Image Processing, vol. 5032, pp. 375–383 (2003)
Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. Tech. rep. Standford University (2004)
Sjöstrand, K., Stegmann, M.B., Larsen, R.: Sparse principal component analysis in medical shape modeling. SPIE Med. Imag.: Image Processing 6144, 61444X (2006)
Stegmann, M.B., Sjöstrand, K., Larsen, R.: Sparse modeling of landmark and texture variability using the orthomax criterion. SPIE Med. Imag.: Image Processing 6144, 61441G (2006)
Bosch, J.G., Mitchell, S.C., Lelieveldt, B.P.F., Nijland, F., Kamp, O., Sonka, M., Reiber, J.H.C.: Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Trans. Med. Imag. 21(11), 1374–1383 (2002)
Browne, M.W.: An overview of analytic rotation in exploratory factor analysis. Multivar. Behav. Res. 36(1), 111–150 (2001)
Kaiser, H.F.: The varimax criterion for analytic rotation in factor analysis. Psychometrika 23(3), 187–200 (1958)
Crawford, C.B., Ferguson, G.A.: A general rotation criterion and its use in orthogonal rotation. Psychometrika 35(3), 321–332 (1970)
Nijland, F., Kamp, O., Verhorst, P.M.J., de Voogt, W.G., Bosch, H.G., Visser, C.A.: Myocardial viability: impact on left ventricular dilatation after acute myocardial infarction. Heart 87, 17–22 (2002)
Bosch, H.G., van Burken, G., Nijland, F., Reiber, J.H.C.: Overview of automated quantitation techniques in 2D echocardiography. In: Reiber, J.H.C., van der Wall, E.E. (eds.) What’s New in Cardiovascular Imaging? Kluwer Academic Publishers, Boston (1998)
Suinesiaputra, A., Frangi, A.F., Üzümcü, M., Reiber, J.H.C., Lelieveldt, B.P.F.: Extraction of myocardial contractility patterns from short-axis MR images using independent component analysis. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds.) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. LNCS, vol. 3117, pp. 75–86. Springer, Heidelberg (2004)
Webb, A.R.: Statistical Pattern Recognition, 2nd edn. John Wiley & Sons Ltd, New York (2002)
Jolliffe, I.T.: Rotation of ill-defined principal component analysis. Appl. Statist. 38(1), 139–147 (1989)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Leung, K.Y.E., Bosch, J.G. (2007). Local Wall-Motion Classification in Echocardiograms Using Shape Models and Orthomax Rotations. In: Sachse, F.B., Seemann, G. (eds) Functional Imaging and Modeling of the Heart. FIMH 2007. Lecture Notes in Computer Science, vol 4466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72907-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-72907-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72906-8
Online ISBN: 978-3-540-72907-5
eBook Packages: Computer ScienceComputer Science (R0)