Abstract
Deformable registration for images with different contrast-enhancement and hence different structure appearance is extremely challenging due to the ill-posed nature of the problem. Utilizing prior anatomical knowledge is thus necessary to eliminate implausible deformations. Landmark constraints and statistically constrained models have shown encouraging results. However, these methods do not utilize the segmentation information that may be readily available. In this paper, we explore the possibility of utilizing such information. We propose to generate an anatomical correlation-regularized deformation field prior by registration of point sets using mixture of Gaussians based on a thin-plate spline parametric model. The point sets are extracted from the segmented object surface and no explicit landmark matching is required. The prior is then incorporated with an intensity-based similarity measure in the deformable registration process using the variational framework. The proposed prior does not require any training data set thus excluding any inter-subject variations compared to learning-based methods. In the experiments, we show that our method increases the registration robustness and accuracy on 12 sets of TAVI patient data, 8 myocardial perfusion MRI sequences, and one simulated pre- and post- tumor resection MRI.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Sorzano, C.O., Thevenaz, P., Unser, M.: Elastic registration of biological images using vector-spline regularization. TBME 52, 652–663 (2005)
Papademetris, X., Jackowski, A., Schultz, R., Staib, L., Duncan, J.: Integrated intensity and point-feature nonrigid registration. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 763–770. Springer, Heidelberg (2004)
Mitra, J., Kato, Z., Martí, R., Oliver, A., Lladó, X., Sidibé, D., Ghose, S., Vilanova, J., Comet, J., Meriaudeau, F.: A spline-based non-linear diffeomorphism for multimodal prostate registration. MIA (2012)
Wang, Y., Staib, L., et al.: Physical model-based non-rigid registration incorporating statistical shape information. MIA 4, 7–20 (2000)
Rueckert, D., Frangi, A., Schnabel, J.: Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. TMI 22, 1014–1025 (2003)
Xue, Z., Shen, D., Davatzikos, C., et al.: Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping. MIA 10, 740–751 (2006)
Lu, Y., Sun, Y., Liao, R., Ong, S.H.: Registration of pre-operative CT and non-contrast-enhanced C-arm CT: An application to trans-catheter aortic valve implantation (TAVI). In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part II. LNCS, vol. 7725, pp. 268–280. Springer, Heidelberg (2013)
Jian, B., Vemuri, B.: A robust algorithm for point set registration using mixture of Gaussians. In: IEEE ICCV 2005, vol. 2, pp. 1246–1251 (2005)
Rohr, K., Stiehl, H., Sprengel, R., Buzug, T., Weese, J., Kuhn, M.: Landmark-based elastic registration using approximating TPS. TMI 20, 526–534 (2001)
Bookstein, F.L.: Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 567–585 (1989)
Richa, R., Poignet, P., Liu, C.: Efficient 3D tracking for motion compensation in beating heart surgery. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 684–691. Springer, Heidelberg (2008)
Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: Variational methods for multimodal image matching. IJCV 50, 329–343 (2002)
Chefd’hotel, C., Hermosillo, G., Faugeras, O.: Flows of diffeomorphisms for multimodal image registration. In: IEEE ISBI 2002, pp. 753–756 (2002)
Murphy, K., Van Ginneken, B., Reinhardt, J., Kabus, S., Ding, K., Deng, X., Cao, K., Du, K., Christensen, G., Garcia, V., et al.: Evaluation of registration methods on thoracic CT: The empire10 challenge. IEEE TMI 30, 1901 (2011)
Wang, J., Li, F., Li, Q.: Automated segmentation of lungs with severe interstitial lung disease in CT. Medical Physics 36, 4592 (2009)
Miao, S., Liao, R., Pfister, M.: Toward smart utilization of two X-ray images for 2-D/3-D registration applied to abdominal aortic aneurysm interventions. In: IEEE ISBI 2011, vol. 1, pp. 550–555 (2011)
Li, C., Jia, X., Sun, Y.: Improved semi-automated segmentation of cardiac CT and MR images. In: IEEE ISBI 2009, pp. 25–28 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, Y., Sun, Y., Liao, R., Ong, S.H. (2013). Hybrid Multimodal Deformable Registration with a Data-Driven Deformation Prior. In: Liao, H., Linte, C.A., Masamune, K., Peters, T.M., Zheng, G. (eds) Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions. MIAR AE-CAI 2013 2013. Lecture Notes in Computer Science, vol 8090. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40843-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-40843-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40842-7
Online ISBN: 978-3-642-40843-4
eBook Packages: Computer ScienceComputer Science (R0)