Abstract
The aim of this paper is to propose a novel mapping algorithm between 2D images and a 3D volume seeking simultaneously a linear plane transformation and an in-plane dense deformation. We adopt a metric free locally over-parametrized graphical model that combines linear and deformable parameters within a coupled formulation on a 5-dimensional space. Image similarity is encoded in singleton terms, while geometric linear consistency of the solution (common/single plane) and in-plane deformations smoothness are modeled in a pair-wise term. The robustness of the method and its promising results with respect to the state of the art demonstrate the extreme potential of this approach.
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San José Estépar, R., Westin, C., Vosburgh, K.: Towards real time 2d to 3d registration for ultrasound-guided endoscopic and laparoscopic procedures. IJCARS 4(6), 549–560 (2009)
Mercier, L., Del Maestro, R.F., Petrecca, K., Araujo, D., Haegelen, C., Collins, D.L.: Online database of clinical mr and ultrasound images of brain tumors. Medical Physics 39 (2012)
Osechinskiy, S., Kruggel, F.: Slice-to-volume nonrigid registration of histological sections to mr images of the human brain. Anatomy Research International 2011 (2010)
Dalvi, R., Abugharbieh, R.: Fast feature based multi slice to volume registration using phase congruency. In: EMBS, pp. 5390–5393 (2008)
Gill, S., Abolmaesumi, P., Vikal, S., Mousavi, P., Fichtinger, G.: Intraoperative prostate tracking with slice-to-volume registration in MRI. In: SMIT, pp. 154–158 (August 2008)
Glocker, B., Sotiras, A., Komodakis, N., Paragios, N.: Deformable medical image registration: setting the state of the art with discrete methods. Annu. Rev. Biomed. Eng. 13 (2011)
Mahapatra, D., Sun, Y.: Nonrigid registration of dynamic renal MR images using a saliency based MRF model. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 771–779. Springer, Heidelberg (2008)
Zikic, D., Glocker, B., Kutter, O., Groher, M., et al.: Linear intensity-based image registration by markov random fields and discrete optimization. Med. Image Anal. 14(4), 550–562 (2010)
Komodakis, N., Tziritas, G., Paragios, N.: Fast, approximately optimal solutions for single and dynamic MRFs. In: CVPR, pp. 1–8 (2007)
Ishikawa, H.: Transformation of general binary mrf minimization to the first-order case. IEEE Transactions on PAMI 33(6), 1234–1249 (2011)
Komodakis, N., Paragios, N.: Beyond pairwise energies: Efficient optimization for higher-order MRFs. In: CVPR, pp. 2985–2992 (2009)
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Ferrante, E., Paragios, N. (2013). Non-rigid 2D-3D Medical Image Registration Using Markov Random Fields. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_21
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DOI: https://doi.org/10.1007/978-3-642-40760-4_21
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