Abstract
We propose a statistical spline deformation model (SSDM) as a method to solve non-rigid image registration. Within this model, the deformation is expressed using a statistically trained B-spline deformation mesh. The model is trained by principal component analysis of a training set. This approach allows to reduce the number of degrees of freedom needed for non-rigid registration by only retaining the most significant modes of variation observed in the training set.
User-defined transformation components, like affine modes, are merged with the principal components into a unified framework. Optimization proceeds along the transformation components rather then along the individual spline coefficients.
The concept of SSDM’s is applied to the temporal registration of thorax CR-images using pattern intensity as the registration measure. Our results show that, using 30 training pairs, a reduction of 33% is possible in the number of degrees of freedom without deterioration of the result. The same accuracy as without SSDM’s is still achieved after a reduction up to 66% of the degrees of freedom.
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References
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imag. 18, 712–721 (1999)
Meyer, C., Boes, J., Kim, B., Bland, P., Wahl, R., Zasadny, K., Kison, P., Koral, K., Frey, K.: Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin plate spline warped geometric deformations. Medical Image Analysis 1, 195–206 (1997)
Gee, J.C., Bajcsy, R.K.: Elastic matching: Continuum mechanical and probabilistic analysis. In: Toga, A.W. (ed.) Brain Warping, Academic Press, San Diego (1999)
Christensen, G., Rabbitt, R., Miller, M.: Deformable templates using large deformation kinetics. IEEE Transactions on Image Processing 5, 1435–1447 (1996)
Ferrant, M., Warfield, S., Nabavi, A., Jolesz, F., Kikinis, R.: Registration of 3D interoperative MR images of the brain using a finite element biomechanical model. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 19–28. Springer, Heidelberg (2000)
Rueckert, D., Frangi, A., Schnabel, J.: Automatic construction of 3D statistical deformation models using non-rigid registration. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 77–94. Springer, Heidelberg (2001)
Weese, J., Buzug, T.M., Lorenz, C., Fassnacht, C.: An approach to 2D/3D registration of a vertebra in 2D X-ray fluoroscopies with 3D CT images. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 119–128. Springer, Heidelberg (1997)
MATLAB. Version 6.5.0.180913a (R13) (2002)
van Ginneken, B., Frangi, A., Staal, J., ter Haar Romeny, B., Viergever, M.: Automatic detection of abnormalities in chest radiographs using local texture analysis. PhD thesis, Universiteit Utrecht (2001)
Ingber, L.: Very fast simulated annealing. Mathematical and Computer Modelling 12, 967–973 (1989)
Unser, M., Aldroubi, A., Eden, M.: B-spline signal processing: Part I—theory. IEEE Trans. Signal Processing 41, 821–832 (1993)
Unser, M., Aldroubi, A., Eden, M.: B-spline signal processing: Part II—efficient design and applications. IEEE Trans. Signal Processing 41, 834–848 (1993)
Unser, M.: Splines: A perfect fit for signal and image processing. IEEE Signal Processing Mag 16, 22–38 (1999)
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Loeckx, D., Maes, F., Vandermeulen, D., Suetens, P. (2003). Non-rigid Image Registration Using a Statistical Spline Deformation Model. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_39
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DOI: https://doi.org/10.1007/978-3-540-45087-0_39
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