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Registration and Segmentation in Medical Imaging

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Registration and Recognition in Images and Videos

Part of the book series: Studies in Computational Intelligence ((SCI,volume 532))

Abstract

The analysis of medical images plays an increasingly important role in many clinical applications. Different imaging modalities often provide complementary anatomical information about the underlying tissues such as the X-ray attenuation coefficients from X-ray computed tomography (CT), and proton density or proton relaxation times from magnetic resonance (MR) imaging. The images allow clinicians to gather information about the size, shape and spatial relationship between anatomical structures and any pathology, if present. Other imaging modalities provide functional information such as the blood flow or glucose metabolism from positron emission tomography (PET) or single-photon emission tomography (SPECT), and permit clinicians to study the relationship between anatomy and physiology. Finally, histological images provide another important source of information which depicts structures at a microscopic level of resolution.

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References

  1. Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)

    Article  Google Scholar 

  2. Asman, A., Landman, B.A.: Formulating spatially varying performance in the statistical fusion framework. IEEE Transactions on Medical Imaging 31(6), 1326–1336 (2012)

    Article  Google Scholar 

  3. Bajcsy, R., Kovačič, S.: Multiresolution elastic matching. Computer Vision, Graphics and Image Processing 46, 1–21 (1989)

    Article  Google Scholar 

  4. Beg, M.F., Miller, M.I., Younes, A.T.L.: Computing metrics via geodesics on flows of diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)

    Article  Google Scholar 

  5. Bookstein, F.L.: Principal Warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(6), 567–585 (1989)

    Article  MATH  Google Scholar 

  6. Bro-Nielsen, M., Gramkow, C.: Fast fluid registration of medical images. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 267–276. Springer, Heidelberg (1996)

    Google Scholar 

  7. Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Modeling Simulation 4(2), 490–530 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chandrashekara, R., Mohiaddin, R.H., Razavi, R.S., Rueckert, D.: Nonrigid image registration with subdivision lattices: Application to cardiac MR image analysis. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 335–342. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Chandrashekara, R., Mohiaddin, R., Rueckert, D.: Analysis of myocardial motion and strain patterns using a cylindrical B-spline transformation model. In: Ayache, N., Delingette, H. (eds.) IS4TM 2003. LNCS, vol. 2673, pp. 88–99. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  10. Choi, Y., Lee, S.: Injectivity conditions of 2D and 3D uniform cubic B-spline functions. Graphical Models 62(6), 411–427 (2000)

    Article  MATH  Google Scholar 

  11. Christensen, G.E., Rabbitt, R.D., Miller, M.I.: Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing 5(10), 1435–1447 (1996)

    Article  Google Scholar 

  12. Christensen, G.E., Rabbitt, R.D., Miller, M.I., Joshi, S.C., Grenander, U., Coogan, T.A., van Essen, D.C.: Topological properties of smooth anatomic maps. In: Information Processing in Medical Imaging: Proc. 14th International Conference (IPMI 1995), pp. 101–112 (1995)

    Google Scholar 

  13. Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Seutens, P., Marchal, G.: Automated multimodality image registration using information theory. In: Information Processing in Medical Imaging: Proc. 14th International Conference (IPMI 1995), pp. 263–274 (1995)

    Google Scholar 

  14. Collins, D.L., Evans, A.C.: Animal: validation and applications of non-linear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294 (1997)

    Article  Google Scholar 

  15. Collins, D.L., Evans, A.C., Holmes, C., Peters, T.M.: Automatic 3D segmentation of neuro-anatomical structures from MRI. In: Information Processing in Medical Imaging: Proc. 14th International Conference (IPMI 1995), pp. 139–152 (1995)

    Google Scholar 

  16. Commowick, O., Akhondi-Asl, A., Warfield, S.K.: Estimating a reference standard segmentation with spatially varying performance parameters: local map staple. IEEE Transactions on Medical Imaging 31(8), 1593–1606 (2012)

    Article  Google Scholar 

  17. Coquillart, S.: Extended free-form deformation: A sculpturing tool for 3D geometric modelling. Computer Graphics 24(4), 187–196 (1986)

    Article  Google Scholar 

  18. Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. Neuroimage 54, 940–954 (2011)

    Article  Google Scholar 

  19. Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. Computer Vision and Image Understanding 66(2), 207–222 (1997)

    Article  Google Scholar 

  20. Davis, M.H., Khotanzad, A., Flamig, D.P., Harms, S.E.: A physics-based coordinate transformation for 3-D image matching. IEEE Transactions on Medical Imaging 16(3), 317–328 (1997)

    Article  Google Scholar 

  21. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley (1973)

    Google Scholar 

  22. Edwards, P.J., Hill, D.L.G., Little, J.A., Hawkes, D.J.: A three-component deformation model for image-guided surgery. Medical Image Analysis 2(4), 355–367 (1998)

    Article  Google Scholar 

  23. Forsey, D.R., Bartels, R.H.: Hierarchical B-spline refinement. ACM Transactions on Computer Graphics 22(4), 205–212 (1988)

    Google Scholar 

  24. Gee, J.C., Bajcsy, R.K.: Elastic matching: Continuum mechanical and probabilistic analysis. In: Toga, A.W. (ed.) Brain Warping, pp. 183–197. Academic Press (1999)

    Google Scholar 

  25. Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through mrfs and efficient linear programming. Medical Image Analysis 12(6), 731–741 (2008)

    Article  Google Scholar 

  26. Hagenlocker, M., Fujimura, K.: CFFD: a tool for designing flexible shapes. The Visual Computer 14(5/6), 271–287 (1998)

    Article  Google Scholar 

  27. Hajnal, J.V., Hill, D.L.G., Hawkes, D.J. (eds.): Medical Image Registration. CRC Press (2001)

    Google Scholar 

  28. Hajnal, J.V., Saeed, N., Soar, E.J., Oatridge, A., Young, I.R., Bydder, G.M.: A registration and interpolation procedure for subvoxel matching of serially acquired MR images. Journal of Computer Assisted Tomography 19(2), 289–296 (1995)

    Article  Google Scholar 

  29. Hajnal, J.V., Saeed, N., Oatridge, A., Williams, E.J., Young, I.R., Bydder, G.M.: Detection of subtle brain changes using subvoxel registration and subtraction of serial MR images. Journal of Computer Assisted Tomography 19(5), 677–691 (1995)

    Article  Google Scholar 

  30. Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain mri segmentation combining label propagation and decision fusion. Neuroimage 33(1), 115–126 (2006)

    Article  Google Scholar 

  31. Heinrich, M.P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F.V., Brady, S.M., Schnabel, J.A.: MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical Image Analysis 16(7), 1423–1435 (2012)

    Article  Google Scholar 

  32. Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Transcations on Medical Imaging (in press, 2013)

    Google Scholar 

  33. Hellier, P., Barillot, C., Mémin, É., Perex, P.: Hierarchical estimation of a dense deformation field for 3D robust registration. IEEE Transactions on Medical Imaging 20(5), 388–402 (2001)

    Article  Google Scholar 

  34. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)

    Article  Google Scholar 

  35. Klein, S., Staring, M., Pluim, J.: Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE Transactions on Image Processing 16(12), 2879–2890 (2007)

    Article  MathSciNet  Google Scholar 

  36. Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Transactions on Image Processing 12(11), 1427–1442 (2003)

    Article  Google Scholar 

  37. Lee, S., Wolberg, G., Chwa, K.-Y., Shin, S.Y.: Image metamorphosis with scattered feature constraints. IEEE Transactions on Visualization and Computer Graphics 2(4), 337–354 (1996)

    Article  Google Scholar 

  38. Lee, S., Wolberg, G., Shin, S.Y.: Scattered data interpolation with multilevel B-splines. IEEE Transactions on Visualization and Computer Graphics 3(3), 228–244 (1997)

    Article  Google Scholar 

  39. Lee, S., Wolberg, G., Shin, S.Y.: Scattered data interpolation with multilevel B-splines. IEEE Transactions on Visualization and Computer Graphics 3(3), 228–244 (1997)

    Article  Google Scholar 

  40. Lin, N., Duncan, J.S.: Generalized robust point matching using an extended free-form deformation model: Application to cardiac images. In: IEEE International Symposium on Biomedical Imaging (2004)

    Google Scholar 

  41. Loeckx, D., Maes, F., Vandermeulen, D., Suetens, P.: Nonrigid image registration using free-form deformations with a local rigidity constraint. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 639–646. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  42. MacCracken, R., Joy, K.I.: Free-form deformations with lattices of arbitrary topology. In: SIGGRAPH, pp. 181–188 (1996)

    Google Scholar 

  43. Maes, F., Collignon, A., Vandermeulen, D., Marechal, G., Suetens, R.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)

    Article  Google Scholar 

  44. Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)

    Article  Google Scholar 

  45. Mattes, D., Haynor, D.R., Vesselle, H., Lewellen, T.K., Eubank, W.: PET–CT image registration in the chest using free-form deformations. IEEE Transactions on Medical Imaging 22(1), 120–128 (2003)

    Article  Google Scholar 

  46. Miller, M., Christensen, G.E., Amit, Y., Grenander, U.: Mathematical textbook of deformable neuroanatomies. Proc. Natl. Acad. Sci. USA 90, 11944–11948 (1993)

    Article  MATH  Google Scholar 

  47. Modat, M., Ridgway, Z.A.T.G.R., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 98(3), 278–284 (2010)

    Article  Google Scholar 

  48. Rohlfing, T., Maurer, J.C.R.: Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Transactions on Information Technology in Biomedicine 7(1), 16–25 (2003)

    Article  Google Scholar 

  49. Rohlfing, T., Maurer, J.C.R.: Multi-classifier framework for atlas-based image segmentation. Pattern Recognition Letters 26(13), 2070–2079 (2005)

    Article  Google Scholar 

  50. Rohlfing, T., Maurer, J.C.R., Bluemke, D.A., Jacobs, M.A.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Transactions on Medical Imaging 22(6), 730–741 (2003)

    Article  Google Scholar 

  51. Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Transactions on Medical Imaging 30(10), 1852–1862 (2011)

    Article  Google Scholar 

  52. Rueckert, D., Hayes, C., Studholme, C., Summers, P., Leach, M.O., Hawkes, D.J.: Non-rigid registration of breast MR images using mutual information. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1144–1152. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  53. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)

    Article  Google Scholar 

  54. Schnabel, J.A., Rueckert, D., Quist, M., Blackall, J.M., Castellano-Smith, A.D., Hartkens, T., Penney, G.P., Hall, W.A., Liu, H., Truwit, C.L., Gerritsen, F.A., Hill, D.L.G., Hawkes, D.J.: A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 573–581. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  55. Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. SIGGRAPH 20(4), 151–160 (1986)

    Article  Google Scholar 

  56. Shen, D., Davatzikos, C.: Hammer: hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 21(11), 1421–1439 (2002)

    Article  Google Scholar 

  57. Studholme, C., Constable, R.T., Duncan, J.S.: Incorporating an image distortion model in non-rigid alignment of EPI with conventional MRI. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 454–459. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  58. Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1998)

    Article  Google Scholar 

  59. Thirion, J.-P.: Image matching as a diffusion process: An analogy with Maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)

    Article  Google Scholar 

  60. Viola, P.: Alignment By Maximization of Mutual Information. PhD thesis, Massachusetts Institute of Technology. A.I. Technical Report No. 1548 (1995)

    Google Scholar 

  61. Viola, P., Wells, W.M.: Alignment by maximization of mutual information. In: Proc. 5th International Conference on Computer Vision (ICCV 1995), pp. 16–23 (1995)

    Google Scholar 

  62. Wahba, G.: Spline Models for Observational Data. Society for Industrial and Applied Mathematics (1990)

    Google Scholar 

  63. Wang, J., Jiang, T.: Nonrigid registration of brain mri using nurbs. Pattern Recognition Letters 28(2), 214–223 (2007)

    Article  Google Scholar 

  64. Warfield, S.K., Zhou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23(7), 903–921 (2004)

    Article  Google Scholar 

  65. Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vision Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK

    Daniel Rueckert

  2. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK

    Julia A. Schnabel

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  1. Daniel Rueckert
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  2. Julia A. Schnabel
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Correspondence to Daniel Rueckert .

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Editors and Affiliations

  1. University of Cambridge Department of Engineering, Cambridge, United Kingdom

    Roberto Cipolla

  2. Università di Catania Dipartimento di Matematica e Informatica, Catania, Catania, Italy

    Sebastiano Battiato

  3. Università di Catania Dipartimento di Matematica e Informatica, Catania, Italy

    Giovanni Maria Farinella

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Rueckert, D., Schnabel, J.A. (2014). Registration and Segmentation in Medical Imaging. In: Cipolla, R., Battiato, S., Farinella, G. (eds) Registration and Recognition in Images and Videos. Studies in Computational Intelligence, vol 532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44907-9_7

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  • DOI: https://doi.org/10.1007/978-3-642-44907-9_7

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