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All issues Volume 57 / No 1 (January-February 2023) ESAIM: M2AN, 57 1 (2023) 299-328 References
Open Access
Issue
ESAIM: M2AN
Volume 57, Number 1, January-February 2023
Page(s) 299 - 328
DOI https://doi.org/10.1051/m2an/2022080
Published online 21 February 2023
  1. M.A. Viergever, J.B.A. Maintz, S. Kleinc, K. Murphy, M. Staring and J.P.W. Pluim, A survey of medical image registration-under review. Med. Image Anal. 33 (2016) 140–144. [Google Scholar]
  2. J. Salvi, C. Matabosch, D. Fofi and J. Forest, A review of recent range image registration methods with accuracy evaluation. Image Vision Comput. 25 (2007) 578–596. [CrossRef] [Google Scholar]
  3. T. Makela, P. Clarysse, O. Sipila, N. Pauna, Q. Pham, T. Katila and I. Magnin, A review of cardiac image registration methods. IEEE Trans. Med. Imag. 21 (2002) 1011–1021. [CrossRef] [PubMed] [Google Scholar]
  4. N. Chumchob, K. Chen and C. Loeza, A fourth-order variational image registration model and its fast multigrid algorithm. SIAM J. Multiscale Model. Simul. 9 (2011) 89–128. [Google Scholar]
  5. D. Ferreira, E. Ribeiro and C. Barcelos, A variational approach to non-rigid image registration with Bregman divergences and multiple features. Pattern Recognition 77 (2018) 237–247. [Google Scholar]
  6. Z. Nie and X. Yang, Deformable image registration using functions of bounded deformation. IEEE Trans. Med. Imag. 38 (2019) 1488–1500. [CrossRef] [PubMed] [Google Scholar]
  7. G. Hermosillo, C. Hotel and O. Faugeras, Variational methods for multimodal image matching. Int. J. Comput. Vis. 50 (2002) 329–343. [CrossRef] [Google Scholar]
  8. L.C. Evans, Partial Differential Equations. American Mathematical Society, Rhode Island (2010). [Google Scholar]
  9. H. Han, A variational model with fractional-order regularization term arising in registration of diffusion tensor image. Inverse Probl. Imaging 12 (2018) 1263–1291. [CrossRef] [MathSciNet] [Google Scholar]
  10. H. Han and Z. Wang, An alternating direction implicit scheme of a fractional-order diffusion tensor image registration model. Appl. Math. Comput. 256 (2019) 105–118. [Google Scholar]
  11. H. Han and H. Zhou, A variational problem arising in registration of diffusion tensor images. Acta Math. Sci. 37 (2017) 539–554. [CrossRef] [MathSciNet] [Google Scholar]
  12. P. Dupuis, U. Grenander and M.I. Miller, Variational problems on flows of diffeomorphisms for image matching. Quart. Appl. Math. 56 (1998) 587–600. [MathSciNet] [Google Scholar]
  13. A. Budhiraja, P. Dupuis and V. Maroulas, Large deviations for stochastic flows of diffeomorphisms. Bernoulli 16 (2010) 234–257. [CrossRef] [MathSciNet] [Google Scholar]
  14. M.F. Beg, M.I. Miller, A. Trouv and L. Younes, Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61 (2005) 139–157. [CrossRef] [Google Scholar]
  15. M. Bruveris, F. Gay-Balmaz, D.D. Holm and T.S. Ratiu, The momentum map representation of images. J. Nonlinear Sci. 21 (2011) 115–150. [Google Scholar]
  16. J. Li, Y. Shi, G. Tran, I. Dinov, D. Wang and A. Toga, Fast local trust region for diffusion tensor registration using exact reorientation and regularization. IEEE Trans. Med. Imag. 33 (2014) 1–43. [CrossRef] [PubMed] [Google Scholar]
  17. H. Lombaert, L. Grady, X. Pennec, N. Ayache and F. Cheriet, Spectral log-demons: diffeomorphic image registration with very large deformations. Int. J. Comput. Vis. 107 (2014) 254–271. [CrossRef] [Google Scholar]
  18. J.P. Thirion, Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2 (1998) 243–260. [Google Scholar]
  19. T. Vercauteren, X. Pennecb, A. Perchanta and N. Ayacheb, Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45 (2009) 61–72. [Google Scholar]
  20. N. Chumchob, Vectorial total variation-based regularization for variational image registration. IEEE Trans. Image Process. 22 (2013) 4551–4559. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  21. V. Vishnevskiy, T. Gass, G. Szekely, C. Tanner and O. Goksel, Isotropic total variation regularization of displacements in parametric image registration. IEEE Trans. Med. Imag. 36 (2017) 385–395. [CrossRef] [PubMed] [Google Scholar]
  22. D. Zhang and K. Chen, 3D orientation-preserving variational models for accurate image registration. SIAM J. Imaging Sci. 13 (2016) 1653–1691. [Google Scholar]
  23. W. Tian, H. Zhou and W. Deng, A class of second order difference approximation for solving space fractional diffusion equations. Math. Comput. 84 (2015) 1703–1727. [Google Scholar]
  24. Y. Wang, L.M. Lui, X. Gu, K.M. Hayashi, T.F. Chan, A.W. Toga, P.M. Thompson and S.T. Yau, Brain surface conformal parameterization using riemann surface structure. IEEE Trans. Med. Imag. 26 (2007) 853–865. [CrossRef] [PubMed] [Google Scholar]
  25. L.M. Lui, T.W. Wong, W. Zeng, X. Gu, P.M. Thompson, T.F. Chan and S.T. Yau, Optimization of surface registrations using beltrami holomorphic flow. J. Sci. Comput. 50 (2012) 557–585. [Google Scholar]
  26. Y. Wang, L.M. Lui, T.F. Chan and P.M. Thompson, Optimization of brain conformal mapping with landmarks. Med. Image Comput. Comput. Assisted Intervention (MICCAI) 2 (2005) 675–683. [Google Scholar]
  27. P.T. Choi, K.C. Lam and L.M. Lui, FLASH: fast landmark aligned spherical harmonic parameterization for genus-0 closed brain surfaces. SIAM J. Imaging Sci. 8 (2015) 67–94. [Google Scholar]
  28. L.M. Lui and C. Wen, Geometric registration of High-Genus surfaces. SIAM J. Imaging Sci. 7 (2014) 337–365. [Google Scholar]
  29. K.C. Lam and L.M. Lui, Optimized quasiconformal parameterization with user-defined area distortions. Commun. Math. Sci. 15 (2017) 2027–2054. [CrossRef] [MathSciNet] [Google Scholar]
  30. L.M. Lui, S. Thiruvenkadam, Y. Wang, P.M. Thompson and T.F. Chan, Optimized conformal surface registration with shape-based landmark matching. SIAM J. Imaging Sci. 3 (2010) 52–78. [Google Scholar]
  31. H. Han and Z. Wang, A diffeomorphic image registration model with fractional-order regularization and Cauchy-Riemann constraint. SIAM J. Imaging Sci. 13 (2020) 1240–1271. [Google Scholar]
  32. H. Han and A. Wang, A fast multi grid algorithm for 2D diffeomorphic image registration model. J Comput. Appl. Math. 394 (2021) 113576. [CrossRef] [Google Scholar]
  33. H. Han, Z. Wang and Y. Zhang, Multiscale approach for Two-Dimensional diffeomorphic image registration. SIAM J. Multiscale Model. Simul. 19 (2021) 1538–1572. [Google Scholar]
  34. F.F. Wu and C.A. Desoer, Global inverse function theorem. IEEE Trans. Circuit Theory 19 (1972) 199–201. [CrossRef] [MathSciNet] [Google Scholar]
  35. J. Zhang, K. Chen and B. Yu, A novel high-order functional based image registration model with inequality constraint. Comput. Math. Appl. 72 (2016) 2887–2899. [CrossRef] [MathSciNet] [Google Scholar]
  36. Y.T. Lee, K.C. Lam and L.M. Lui, Landmark-matching transformation with large deformation via n-dimensional quasi-conformal maps. J. Sci. Comput. 67 (2016) 926–954. [Google Scholar]
  37. G.P. Paillé and P. Poulin, As-conformal-as-possible discrete volumetric mapping. Comput. Graphics 36 (2012) 427–433. [CrossRef] [Google Scholar]
  38. J. Zhang and K. Chen, Variational image registration by a total fractional-order variation model. J. Comput. Phys. 293 (2015) 442–461. [CrossRef] [MathSciNet] [Google Scholar]
  39. F. Demengel, G. Demengel and R. Erne, Functional Spaces for the Theory of Elliptic Partial Differential Equations. Springer, London (2012). [CrossRef] [Google Scholar]
  40. J.B. Conway, A Course in Functional Analysis. Springer, New York (2007). [CrossRef] [Google Scholar]
  41. C. Kanzow and Y. Shehu, Strong convergence of a double projection-type method for monotone variational inequalities in Hilbert spaces. J. Fixed Point Theory Appl. 20 (2018) 51–72. [CrossRef] [Google Scholar]
  42. T.L. Friesz, Finite Dimensional Variational Inequalities and Nash Equilibria. Springer US, New York (2010). [Google Scholar]
  43. H. Han, A tensor voting based fractional-order image denoising model and its numerical algorithm. Appl. Numer. Math. 145 (2019) 133–144. [CrossRef] [MathSciNet] [Google Scholar]
  44. B. He, X. Yuan and W. Zhang, A customized proximal point algorithm for convex minimization with linear constraints. Comput. Optim. Appl. 56 (2013) 559–572. [CrossRef] [MathSciNet] [Google Scholar]
  45. G. Gu, B. He and X. Yuan, Customized proximal point algorithms for linearly constrained convex minimization and saddle-point problems: a unified approach. Comput. Optim. Appl. 59 (2014) 135–161. [CrossRef] [MathSciNet] [Google Scholar]
  46. B. He and X. Yuan, Convergence analysis of primal-dual algorithms for a saddle point problem: from contraction perspective. SIAM J. Imaging Sci. 5 (2012) 119–149. [Google Scholar]
  47. A. Chambolle and T. Pock, A first-order primal-dual algorithms for convex problem with applications to imaging. J. Math. Imaging Vison 40 (2011) 120–145. [Google Scholar]
  48. N. Gould and P.L. Toint, Preprocessing for quadratic programming. Math. Program. 100 (2004) 95–132. [Google Scholar]
  49. BrainWeb: Simulated Brain Database, https://brainweb.bic.mni.mcgill.ca/brainweb/ (2013). [Google Scholar]
  50. 3D-IRCADb-02, https://www.ircad.fr/research/3d-ircadb-02/ (2019). [Google Scholar]
  51. Imlab-uiip/lung-segmentation-3d, https://github.com/imlab-uiip/lung-segmentation-3d/find/master (2019). [Google Scholar]
  52. H. Lombaert, Diffeomorphic Log Demons Image Registration (2012). [Google Scholar]
  53. Medical Image Registration Toolbox, http://www.bme.ogi.edu/myron/matlab/MIRT/ (2016). [Google Scholar]
  54. H. Han, Z. Wang and Y. Zhang, Multiscale approach for Three-Dimensional conformal image registration. SIAM J. Imaging Sci. 15 (2022) 1431–1468. [Google Scholar]
  55. Z. Wang, A. Bovik, H. Sheikh and E. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 (2004) 600–612. [CrossRef] [Google Scholar]
  56. E. Agostino, F. Maes, D. Vandermeulen and P. Suetens, Viscous fluid model for multimodal non-rigid image registration using mutual information. Med. Image Anal. 7 (2003) 565–575. [Google Scholar]
  57. W. Bao, Y. Cai and X. Ruan, Groud states of Bose-Einstein condensates with higher order interaction. Phys. D 386387 (2019) 38–48. [Google Scholar]
  58. M. Burger, J. Modersitzki and L. Ruthotto, A hyperelastic regularization energy for image registration. SIAM J. Sci. Comput. 35 (2013) 132–148. [Google Scholar]
  59. M. Droske and M. Rumpf, A variational approach to nonrigid morphological image registration. SIAM J. Appl. Math. 64 (2004) 668–687. [Google Scholar]
  60. J. Zhang, K. Chen and B. Yu, An improved discontinuity-preserving image registration model and its fast algorithm. Appl. Math. Model. 40 (2016) 10740–10759. [CrossRef] [MathSciNet] [Google Scholar]

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