Abstract
This paper presents a novel method for segmentation of cardiac perfusion MRI. By performing complex analyses of variance and clustering in an annotated training set off-line, the presented method provides real-time segmentation in an on-line setting. This renders the method feasible for e.g. analysis of large image databases or for live non-rigid motion-compensation in modern MR scanners. Changes in image intensity during the bolus passage is modelled by an Active Appearance Model augmented with a cluster analysis of the training set and priors on pose and shape. Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Quantitative and qualitative results show high accuracy, given the limited number of subjects.
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References
R. Beichel, S. Mitchell, E. Sorantin, F. Leberl, A. Goshtasby, and M. Sonka. Shape-and appearance-based segmentation of volumetric medical images. IEEE International Conference on Image Processing, 2:589–592, 2001.
L.M. Bidaut and J.P. Vallee. Automated registration of dynamic MR images for the quantification of myocardial perfusion. Magn Reson Imag, 13(4):648–655, 2001.
J.G. Bosch, S.C. Mitchell, B.P. Lelieveldt, F. Nijland, O. Kamp, M. Sonka, and J.H. Reiber. Fully automated endocardial contour detection in time sequences of echocardiograms by three-dimensional active appearance models. Medical Imaging 2002: Image Processing, San Diego CA, SPIE, pages 452–462, 2002.
T.F. Cootes, G.J. Edwards, and C.J. Taylor. Active appearance models. In Proc. European Conf. on Computer Vision, volume 2, pages 484–498. Springer, 1998.
T.F. Cootes, G.J. Edwards, and C.J. Taylor. Active appearance models. IEEE Trans. on Pattern Recognition and Machine Intelligence, 23(6):681–685, 2001.
T.F. Cootes and C.J. Taylor. Statistical Models of Appearance for Computer Vision. Tech. Report. Feb 2000, University of Manchester, 2000.
G.J. Edwards, C.J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition, pages 300–5. IEEE Comput. Soc, 1998.
E. Forgey. Cluster analysis of multivariate data. Biometrics, 21:768, 1965.
HenrikB.W. Larsson, Thomas Fritz-Hansen, Egill Rostrup, Lars Søndergaard, Poul Ring, and Ole Henriksen. Myocardial perfusion modeling using MRI. Magnetic Resonance in Medicine, 35:716–726, 1996.
S. Mitchell, B. Lelieveldt, R. Geest, H. Bosch, J. Reiber, and M. Sonka. Time continuous segmentation of cardiac MR image sequences using active appearance motion models. In Medical Imaging 2001: Image Processing, San Diego CA, SPIE, volume 1, pages 249–256. SPIE, 2001.
S. Mitchell, B. Lelieveldt, R. Geest, J. Schaap, J. Reiber, and M. Sonka. Segmentation of cardiac MR images: An active appearance model approach. In Medical Imaging 2000: Image Processing, San Diego CA, SPIE, volume 1. SPIE, 2000.
L. Spreeuwers and M. Breeuwer. Automatic detection of the myocardial boundaries of the right and left ventricle in mr cardio perfusion scans. Proceedings of SPIE-The International Society for Optical Engineering, 4322(3):1207–1217, 2001.
M.B. Stegmann and R.H. Davies. Corpus callosum analysis using MDL-based sequential models of shape and appearance. In Medical Image Computing and Computer-Assisted Intervention-MICCAI., LNCS. Springer, 2003 (submitted).
M.B. Stegmann, B K. Ersbøll, and R. Larsen. FAME-a flexible appearance modelling environment. IEEE Trans. on Medical Imaging, 2003 (to appear).
M.B. Stegmann, R. Fisker, and B.K. Ersbøll. Extending and applying active appearance models for automated, high precision segmentation in diffierent image modalities. In Proc. 12th Scandinavian Conference on Image Analysis-SCIA 2001, volume 1, pages 90–97, 2001.
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Stegmann, M.B., Larsson, H.B.W. (2003). Motion-Compensation of Cardiac Perfusion MRI Using a Statistical Texture Ensemble. In: Magnin, I.E., Montagnat, J., Clarysse, P., Nenonen, J., Katila, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2003. Lecture Notes in Computer Science, vol 2674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44883-7_16
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DOI: https://doi.org/10.1007/3-540-44883-7_16
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