Advertisement

Registration and Normalization

  • Klaus D. Toennies
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Information about an object from different sources can be combined if a transformation allows mapping data from one source to data of the other source. In medical imaging, the two sources are image acquisition systems. If the two sources depict the same subject, this process is called registration. If they depict different subjects, it is called normalization. The mapping is a geometric transformation that accounts for different positioning of a patient in two image acquisition systems. Determining a registration or normalization transformation requires redundant information in the two images, a suitable restriction of acceptable transformations, and, for iterative schemes, a criterion that rates the quality of a given transformation. Various ways to compute a registration or normalization transformation from medical images will be discussed in this chapter.

Keywords

Mutual Information Displacement Field Rigid Registration Iterative Close Point Algorithm Procrustes Distance 
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

  1. Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9(5):698–700CrossRefGoogle Scholar
  2. Bajcy R, Lieberson R, Reivich M (1983) A computerized system for the elastic matching of deformed radiographic images to idealized atlas images. J Comput Assist Tomogr 7(4):618–625CrossRefGoogle Scholar
  3. Bajcy R, Kovacic S (1989) Multiresolution elastic matching. Comput Vision Graph Image Process 46(1):1–21CrossRefGoogle Scholar
  4. Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256CrossRefGoogle Scholar
  5. Brown LF (1992) A survey of image registration techniques. ACM Comput Surv 24(4):325–376CrossRefGoogle Scholar
  6. Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: Proceedings of 8th European conference computer vision ECCV 2004. LNCS, vol 3024(4), pp 25–36Google Scholar
  7. Chui H, Rangarajan A (2003) A new point matching algorithm for non-rigid registration. Comput Vis Image Underst 89:114–141CrossRefMATHGoogle Scholar
  8. Cootes TF, Beeston C, Edwards GJ, Taylor CJ (1999) A unified framework for atlas matching using active appearance models. In: Proceedings of 16th international conference information processing in medical imaging IPMI’99. LNCS, vol 1613, pp 322–333Google Scholar
  9. Crum WR, Hartkens T, Hill DLG (2014) Non-rigid image registration: theory and practice. Br J Radiol 77(Suppl. 2):S140–S153Google Scholar
  10. Cyr CM, Kamal AF, Sebastian TB, Kimia BB (2000) 2d-3d registration based on shape matching. In: IEEE workshop math methods in biomedical image analysis, pp 198–203Google Scholar
  11. Dawant B (2002) Nonrigid registration of medical images: purpose and methods, a short survey. In: Proceedings of IEEE international symposium biomedical imaging, pp 465–468Google Scholar
  12. du Bois d’Aische A, Craene MD, Geets X, Gregoire V, Macq B, Warfield SK (2005) Efficient multi-modal dense field non-rigid registration: alignment of histological and section images. Med Image Anal 9(6):538–546CrossRefGoogle Scholar
  13. Engel K, Brechmann A, Toennies KD (2005) A two-level dynamic model for the representation and recognition of cortical folding patterns. In: IEEE international conference image processing ICIP2005, I, pp 297–300Google Scholar
  14. Fitzpatrick JM, West JB, Maurer CR Jr (1998) Predicting error in rigid-body point-based registration. IEEE Trans Med Imaging 17(5):694–702CrossRefGoogle Scholar
  15. Flach B, Kask E, Schlesinger D, Skulish A (2002) Unifying registration and segmentation for multi-sensor images. In: Pattern recognition 2002,.LNCS, vol 2449, pp 190–197Google Scholar
  16. Glocker B, Komodakis N, Tziritas G, Navab N, Paragios N (2008) Dense image registration through MRFs and efficient linear programming. Med Image Anal 12(6):731–741CrossRefGoogle Scholar
  17. Grimson WEL, Ettinger GJ, White SJ, Lozano-Pérez T, Wells WM III, Kikinis R (1996) An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization. IEEE Trans Med Imaging 15(2):129–140CrossRefGoogle Scholar
  18. Heinrich MP, Jenkinson M, Brady M, Schnabel JA (2010) Discontinuity preserving regularization for the variational optical-flow registration using the modified L p norm. In: Medical image analysis for the clinic—a grand challenge, MICCAI workshop, pp 185–194Google Scholar
  19. Hentschke CM, Serowy S, Janiga G, Rose G, Toennies KD (2010) Estimating blood flow by re-projection of 2d-DSA to 3d-RA data sets for blood flow simulations. Intl J Comput Assist Radiol Surg (CARS) 5(1):342–343Google Scholar
  20. Horn BKP (1986) Robot vision. MIT Press, CambridgeGoogle Scholar
  21. Horn BKP (1987) Closed form solution of absolute orientation using unit quaternions. J Optics Soc Am A 4:629–642CrossRefGoogle Scholar
  22. Jamriška O, Sýkora D, Hornung A (2012) Cache-efficient graph cuts on structured grids. In: IEEE conference on computer vision and pattern recognition (CVPR 2012), pp 3673–3680)Google Scholar
  23. Kiriyanthan S, Fundana K, Majeed T, Cattin PC (2016) Discontinuity preserving image registration through motion segmentation: a primal-dual approach. Comput Math Methods Med ID 9504949Google Scholar
  24. Lavallée S, Szelinski R (1995) Recovering the position and orientation of free-form objects from image contours using 3d distance maps. IEEE Trans Pattern Anal Mach Intell 17(4):378–390CrossRefGoogle Scholar
  25. Lester H, Arridge SR (1999) A survey of hierarchical non-linear medical image registration. Pattern Recogn 32(1):129–149CrossRefGoogle Scholar
  26. Lester H, Arridge SR, Jansons KM, Lemieux L, Hajnal JV, Oatridge A (1999) Non-linear registration with the variable viscosity fluid algorithm. In: Proceedings of 16th international conference on information processing in medical imaging IPMI’99. LNCS, vol 1613, pp 238–251Google Scholar
  27. Maes F, Vandermeulen D, Suetens P (1999) Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Med Image Anal 3(4):373–386CrossRefGoogle Scholar
  28. Maglogiannis I (2004) Automatic segmentation and registration of dermatological images. J Math Model Anal 2(3):277–294MathSciNetGoogle Scholar
  29. Mahapatra D, Sun Y (2010) Joint registration and segmentation of dynamic cardiac perfusion images using MRFs. In: International conference on medical image computing and computer-assisted intervention MICCAI 2010, pp 493–501Google Scholar
  30. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36CrossRefGoogle Scholar
  31. Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16(3):642–661CrossRefGoogle Scholar
  32. Mazziotta JC, Toga AW, Evans A, Fox P, Lancaster J (1995) A probabilistic atlas of the human brain: theory and rationale for its development. Neuroimage 2(2):89–101CrossRefGoogle Scholar
  33. Moderisitzki J (2003) Numerical methods for image registration. Oxford University Press, OxfordCrossRefGoogle Scholar
  34. Oliveira FP, Tavares JMR (2014) Medical image registration: a review. Comput Methods Biomech Biomed Eng 17(2):73–93CrossRefGoogle Scholar
  35. Ourselin S, Roche A, Prima S, Ayache N (2000) Block matching: a general framework to improve robustness of rigid registration of medical images. In: Medical image computing and computer-assisted intervention—MICCAI 2000. LNCS, vol 1935Google Scholar
  36. Pace DF, Aylward SR, Niethammer M (2013) A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs. IEEE Trans Med Imaging 32(11):2114–2126CrossRefGoogle Scholar
  37. Papiez BW, Heinrich MP, Fehrenbach J, Risser L, Schnabel JA (2014) An implicit sliding-motion preserving regularization via bilateral filtering for deformable image registration. Med Image Anal 18:1299–1311CrossRefGoogle Scholar
  38. Parisot S, Duffau H, Chemouny S, Paragios N (2012) Joint tumor segmentation and dense deformable registration of brain MR images. In: International conference on medical image computing and computer-assisted intervention, MICCAI 2012, pp 651–658Google Scholar
  39. Pelizzari CA, Chen GT, Spelbring DR, Weichselbaum RR, Chen CT (1989) Accurate three-dimensional registration of CT, PET, and/or MR images of the brain. J Comput Assist Tomogr 13(1):20–26CrossRefGoogle Scholar
  40. Penney GP, Weese J, Little JA, Desmedt P, Hill DLG, Hawkes DJ (1998) A comparison of similarity measures for use in 2-d–3-d medical image registration. IEEE Trans Med Imaging 17(4):586–595CrossRefGoogle Scholar
  41. Pluim JBW, Maintz JBA, Viergever MA (2003) Mutual-information based registration of medical images: a survey. IEEE Trans Med Imaging 22(8):986–1004CrossRefMATHGoogle Scholar
  42. Pohl KM, Fisher J, Grimson WEL, Kikinis R, Wells WM (2006) A Bayesian model for joint segmentation and registration. NeuroImage 31:228–239CrossRefGoogle Scholar
  43. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in C: the art of scientific computing, 2nd edn. Cambridge University Press, CambridgeMATHGoogle Scholar
  44. Reddy BS, Chatterji BN (1996) An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans Image Process 5(8):1266–1271CrossRefGoogle Scholar
  45. Rexilius J, Warfield SK, Guttmann CRG, Wei X, Benson R, Wolfson L, Shenton M, Handels H, Kikinis K (2001) A novel nonrigid registration algorithm and applications. In: Proceedings of 4th international conference on medical image computing and computer-assisted intervention—MICCAI 2001. LNCS, vol 2208, pp 923–931Google Scholar
  46. Roland PE, Geyer S, Amunts K, Schormann T, Schleicher A, Malikovic A, Zilles K (1997) Cytoarchitectural maps of the human brain in standard anatomical space. Hum Brain Mapp 5:222–227CrossRefGoogle Scholar
  47. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721CrossRefGoogle Scholar
  48. Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm. In: 3rd international conference on 3-d imaging and modeling (3DIM’01), pp 145–152Google Scholar
  49. Russakoff DB, Rohlfing T, Maurer CR Jr (2003) Fast intensity-based 2d-3d image registration of clinical data using light fields. In: Proceedings of 9th international conference on computer vision (ICCV2003), pp 416–422Google Scholar
  50. Sandor S, Leahy R (1997) Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Trans Med Imaging 16(1):41–54CrossRefGoogle Scholar
  51. Sotiras A, Davatzikos C, Paragios N (2013) Deformable medical image registration: a survey. IEEE Trans Med Imaging 32(7):1153–1190CrossRefGoogle Scholar
  52. Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system—an approach to cerebral imaging. Thieme Medical Publishers, StuttgartGoogle Scholar
  53. Thévenaz P, Unser M (2000) Optimization of mutual information for multiresolution image registration. IEEE Trans Image Process 9(12):2083–2099CrossRefMATHGoogle Scholar
  54. Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2(3):243–260CrossRefGoogle Scholar
  55. Toga AW (1999) Brain warping. Academic Press, MassachusettsGoogle Scholar
  56. Toennies KD, Udupa JK, Herman GT, Wornum IL III, Buchman SR (1990) Registration of 3-d objects and surfaces. IEEE Comput Graph Appl 10(3):52–62CrossRefGoogle Scholar
  57. Venot A, Leclerc V (1984) Automated correction of patient motion and gray values prior to subtraction in digitized angiography. IEEE Trans Med Imaging 3(4):179–186CrossRefGoogle Scholar
  58. Vercauteren T, Pennec X, Perchant A, Ayache N (2008) Symmetric log-domain diffeomorphic registration: A demons-based approach. In: International conference on medical image computing and computer-assisted intervention, pp 754–761Google Scholar
  59. Weese J, Penney GP, Buzug TM, Fassnacht C, Lorenz C (1997) 2D/3D registration of pre-operative CT images and intra-operative X-ray projections for image guided surgery. In: Proceedings international symposium computer assisted radiology and surgery (CARS97), pp 833–838Google Scholar
  60. West J, Fitzpatrick JM et al (1997) Comparison and evaluation of retrospective intermodality brain image registration techniques. J Comp Ass Tomogr 21(4):554–568CrossRefGoogle Scholar
  61. Wu Z, Rietzel E, Boldea V, Sarrut D, Sharp GC (2008) Evaluation of deformable registration of patient lung 4d CT with subanatomical region segmentation. Med Phys 35:775–781CrossRefGoogle Scholar
  62. Wyatt PP, Noble JA (2003) MAP MRF joint segmentation and registration of medical images. Med Image Anal 7:539–552CrossRefGoogle Scholar
  63. Xu X, Dony RD (2004) Differential evolution with Powell’s direction set method in medical image registration. In: IEEE international symposium biomedical imaging, I, pp 732–735Google Scholar
  64. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Computer Science Department, ISGOtto-von-Guericke-Universität MagdeburgMagdeburgGermany

Personalised recommendations