Abstract
In this paper, we propose a new feature based non-rigid image registration method for dealing with two important issues. First, in order to establish reliable anatomical correspondence between template and subject images, efficient and distinctive region descriptor is needed as intensity information alone maybe insufficient. Second, since interference factors such as monotonic gray-level bias fields are commonly existed during the imaging process, the registration algorithm should be robust against such factors. There are two main contributions presented in this paper. (1) A new region descriptor, named uniform gradient spherical pattern (UGSP), is proposed to extract the geometric features from input images. UGSP encodes second order voxel interaction information. (2) The UGSP feature is rotation and monotonic gray-level bias field invariant. The proposed method is integrated with the Markov random field (MRF) labeling framework to formulate the registration process. The α-expansion algorithm is used to optimize the corresponding MRF energy function. The proposed method is evaluated on both the simulated and real 3D databases obtained from BrainWeb and IBSR respectively and compared with other state-of-the-art registration methods. Experimental results show that the proposed method gives the highest registration accuracy among all the compared methods on both databases.
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Rohr, K.: Image registration based on thin-plate splines and local estimates of anisotropic landmark localization uncertainties. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1174–1183. Springer, Heidelberg (1998)
Thompson, P., Toga, A.W.: A surface-based technique for warping three-dimensional images of the brain. TMI 15, 402–417 (1996)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: Application to breast MR images. TMI 18, 712–721 (1999)
Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s demons. MedIA 2, 243–260 (1998)
Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. TMI 21, 1421–1439 (2002)
Tu, Z., Narr, K., Dollár, P., Dinov, I., Thompson, P., Toga, A.: Brain anatomical structure segmentation by hybrid discriminative/generative models. TMI 27, 495–508 (2007)
Dirk, L., Slagmolen, P., Maes, F., Vandermeulen, D., Suetens, P.: Nonrigid image registration using conditional mutual information. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 725–737. Springer, Heidelberg (2007)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24, 971–987 (2002)
Yershova, A., LaValle, S.: Deterministic sampling methods for spheres and so(3). In: ICRA, pp. 3974–3980 (2004)
Wu, G., Qi, F., Shen, D.: Learning-based deformable registration of mr brain images. TMI 25, 1145–1157 (2006)
Glocker, B., Komodakis, N., Paragios, N., Tziritas, G., Navab, N.: Inter and intra-modal deformable registration: Continuous deformations meet efficient optimal linear programming. In: IPMI, pp. 408–420 (2007)
Tang, T.W.H., Chung, A.C.S.: Non-rigid image registration using graph-cuts. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 916–924. Springer, Heidelberg (2007)
Yuri, B., Olga, V., Ramin, Z.: Fast approximate energy minimization via graph cuts. PAMI 23, 1222–1239 (2001)
Crum, W.R., Rueckert, D., Jenkinson, M., Kennedy, D., Smith, S.M.: A framework for detailed objective comparison of non-rigid registration algorithms in neuroimaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 679–686. Springer, Heidelberg (2004)
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Liao, S., Chung, A.C.S. (2009). Non-rigid Image Registration with Uniform Gradient Spherical Patterns. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_86
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