A Robust and Accurate Non-rigid Medical Image Registration Algorithm Based on Multi-level Deformable Model
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Abstract
Background: Compared to the rigid image registration task, the non-rigid image registration task faces much more challenges due to its high degree of freedom and inherent requirement of smoothness in the deformation field. The purpose was to propose an efficient coarse-to-fine non-rigid medical image registration algorithm based on a multi-level deformable model.
Methods: In this paper, a robust and efficient coarse-to-fine non-rigid medical image registration algorithm is proposed. It contains three level deformation models, i.e., the global homography model, the local mesh-level homography model, and the local B-spline FFD (Free-Form Deformation) model. The coarse registration is achieved by the first two level models. In the global homography model, a robust algorithm for simultaneous outliers (error matched feature points) removal and model estimation is applied. In the local mesh-level homography model, a new similarity measure is proposed to improve the robustness and accuracy of local mesh based registration. In the fine registration, a local B-spline FFD model with normalized mutual information gradient is employed.
Results: We verified the effectiveness of each stage of the proposed registration algorithm with many non-rigid transformation image pairs, and quantitatively compared our proposed registration algorithm with the HBFFD method which is based on the control points of multi-resolution. The experimental results show that our algorithm is more accurate than the hierarchical local B-spline FFD method.
Conclusion: Our algorithm can achieve high precision registration by coarse-to-fine process based on multi-level deformable model, which ourperforms the state-of-the-art methods.
Medha V, Wyawahare, Pradeep M. Patil, Hemant K. Abhyankar (2009). Image Registration Techniques: An overview. Int J Signal Processing Pattern Recognition, 2: 11-27.
Kanekoa S, Yutaka S, Satoru I (2003). Using selective correlation coefficient for robust image registration. Pattern Recognition, 19: 1165-73.
Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997). Multimo-dality image registration by maximization of mutual information. IEEE Trans Med Imaging, 16: 187–98.
Bardera A, Feixas M, Boada I, Sbert M (2006). High-dimensional normalized mutual information for image registration using random lines. In: International Workshop on Biomedical Image Regis-tration, 4057: 264-71.
Andronache A, Von-Siebenthal MG, Cattin P (2008). Non-rigid registration of multi-modal images using both mutual infor-mation and cross-correlation. Med Image Anal, 12: 3-15.
Pluim J, Maintz JA, Viergever M (2003). Mu-tual information based registration of medical images: a survey. IEEE Trans Med Imaging, 22: 986–1004.
Zhang J, Wang J, Wang X, Feng D (2015). Multimodal image registration with joint structure tensor and local entropy. Int J Comput Assist Radiol Surg, 10:1765-75.
Holden M (2008). A review of geometric transformations for nonrigid body regis-tration. IEEE Trans Med Imaging, 27: 111-28.
Sotiras A, Davatzikos C, Paragios N (2013). Deformable medical image registration: a survey. IEEE Trans Med Imaging, 32:1153-90.
He J, Christensen GE (2003). Large defor-mation inverse consistent elastic image registration. In: International Conference on Information Processing in Medical Imaging, 18: 438-49.
Le Guyader C, Vese LA (2011). A combined segmentation and registration framework with a nonlinear elasticity smoother. Com-puter Vision and Image Understanding, 115: 1689-709.
Lu X, Zhao Y, Zhang B, Wu JS, Li N, Jia WT (2013). A non-rigid cardiac image registration method based on an optical flow model. Int J Light Electron Optics, 124: 4266-73.
Thirion JP (1998). Image matching as a dif-fusion process: an analogy with Max-well's demons. Med Image Anal, 2: 243-60.
Rueckert D, Sonoda LI, Hayes C, Hill D, Leach M, Hawkes D (1999). Non-rigid registration using free-form defor-mations: application to breast MR imag-es. IEEE Trans Med Imaging, 18: 712–21.
Rohde GK, Aldroubi A, Dawant BM (2003). The adaptive bases algorithm for intensi-ty-based nonrigid image registration. IEEE Trans Med Imaging, 22:1470-9.
Chui H, Rangarajan A (2003). A new point matching algorithm for non-rigid regis-tration. Computer Vision Image Understand, 89: 114–41.
Datteri RD, Liu Y, D'Haese PF, Dawant BM (2015). Validation of a nonrigid registra-tion error detection algorithm using clini-cal MRI brain data. IEEE Trans Med Imag-ing, 34:86-96.
Hellier P, Barillot C (2003). Coupling dense and landmark-based approaches for non-rigid registration. IEEE Trans Med Imaging, 22:217–27.
Wu G, Yap PT, Kim M, Shen D (2010). TPS-HAMMER: improving HAMMER registration algorithm by soft corre-spondence matching and thin-plate splines based deformation interpolation. NeuroImage, 49: 2225–33.
Lowe DG (2004). Distinctive Image Fea-tures from Scale-Invariant Keypoints. Int J Computer Vision, 60: 91-110.
Mikolajczyk K, Schmid C (2005). Perfor-mance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell, 27: 1615-30.
Liu S, Yuan L, Tan P, Sun J (2013). Bundled camera paths for video stabilization. Acm Transactions on Graphic, 32:1-10.
Moisan L, Moulon P, Monasse P (2012). Automatic Homographic Registration of a Pair of Images, with A Contrario Elim-ination of Outliers. Image Processing on Line, 2: 56-73.
Yushkevich PA, Wang HZ, Pluta J, Das SR, Craige C, Avants BB, Weiner MW, Mueller S (2010). Nearly automatic seg-mentation of hippocampal subfields in in vivo focal T2-weighted MRI. Neuroimage, 53: 1208-24.
Suh JW, Scheinost D, Dione DP, Dobrucki LW, Sinusas AJ, Papademetris X (2011). A non-rigid registration method for serial lower extremity hybrid SPECT/CT imag-ing. Med Image Anal, 15:96-111.
Cocosco CA, Kollokian V, Kwan RKS, Ev-ans AC (1997). Brain Web: Online Inter-face to a 3D MRI Simulated Brain Data-base. Neuroimage, 5 :425.
| Files | ||
| Issue | Vol 46 No 12 (2017) | |
| Section | Original Article(s) | |
| Keywords | ||
| Non-rigid registration Medical image Deformable model | ||
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