Skip to main content
SpringerLink
Log in

Similarity and Dissimilarity Measures

  • Chapter
Image Registration

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The topics of similarity and dissimilarity measures are discussed in detail. The chapter starts with definitions of similarity and dissimilarity measures and lists the requirements for them to be metrics. In addition to the existing similarity and dissimilarity measures, 3 new similarity measures and 1 new dissimilarity measure are introduced. The performances of 16 similarity measures and 10 dissimilarity measures in image matching are determined and compared, and their sensitivities to noise and blurring as well as to intensity and geometric changes are also determined and compared. The similarity measures tested are Pearson correlation, Tanimoto measure, stochastic sign change, deterministic sign change, minimum ratio, Spearman’s ρ, Kendall’s τ, greatest deviation, ordinal measure, correlation ratio, energy of joint probability density, material similarity, Shannon mutual information, Rényi mutual information, Tsallis mutual information, and I α information. The dissimilarity measures tested are L 1 norm, median of absolute differences, square L 2 norm, median of square differences, normalized square L 2 norm, incremental sign distance, intensity-ratio variance, intensity-mapping-ratio variance, rank distance, joint entropy, and exclusive F-information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alliney, S., Morandi, C.: Digital image registration using projections. IEEE Trans. Pattern Anal. Mach. Intell. 8(2), 222–233 (1986)

    Article  Google Scholar 

  2. Alvarez, L., Lions, P.-L., Morel, J.-M.: Image selective smoothing and edge detection by nonlinear diffusion II. SIAM J. Numer. Anal. 29(3), 845–866 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  3. Anuta, P.E.: Spatial registration of multispectral and multitemporal digital imagery using fast Fourier transform techniques. IEEE Trans. Geosci. Electron. 8(4), 353–368 (1970)

    Article  Google Scholar 

  4. Ayinde, O., Yang, Y.-H.: Face recognition approach based on rank correlation of gabor-filtered images. Pattern Recognit. 35, 1275–1289 (2002)

    Article  MATH  Google Scholar 

  5. Barnea, D.I., Silverman, H.F.: A class of algorithms for fast digital image registration. IEEE Trans. Comput. 21(2), 179–186 (1972)

    Article  MATH  Google Scholar 

  6. Bhat, N., Nayar, S.K.: Ordinal measures for image correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 20(4), 415–423 (1998)

    Article  Google Scholar 

  7. Blakeman, J.: On tests for linearity of regression in frequency distributions. Biometrika 4(3), 332–350 (1905)

    Article  Google Scholar 

  8. Borland, L., Plastino, A.R., Tsallis, C.: Information gain within nonextensive thermostatistics. J. Math. Phys. 39(12), 6490–6501 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chambon, S., Crouzil, A.: Dense matching using correlation: new measures that are robust near occlusions. In: Proc. British Machine Vision Conference, vol. 1, pp. 143–152 (2003)

    Google Scholar 

  10. Chambon, S., Crouzil, A.: Similarity measures for image matching despite occlusions in stereo vision. Pattern Recognit. 44, 2063–2075 (2011)

    Article  Google Scholar 

  11. Chen, Y.-P.: A note on the relationship between Spearman’s ρ and Kendall’s τ for extreme order statistics. J. Stat. Plan. Inference 137, 2165–2171 (2007)

    Article  MATH  Google Scholar 

  12. Chen, Q.-S.: Matched filtering techniques. In: Le Moigne, J., Netanyahu, N.S., Eastman, R.D. (eds.) Image Registration for Remote Sensing, pp. 112–130. Cambridge University Press, Cambridge (2011)

    Chapter  Google Scholar 

  13. Chen, H.-M., Varshney, P.K., Arora, M.K.: Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Trans. Geosci. Remote Sens. 41(11), 2445–2454 (2003)

    Article  Google Scholar 

  14. Cole-Rhodes, A.A., Johnson, K.L., LeMoigne, J., Zavorin, I.: Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient. IEEE Trans. Image Process. 12(12), 1495–1511 (2003)

    Article  MathSciNet  Google Scholar 

  15. Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, A.: Automated multi-modality image registration based on information theory. In: Proc. Information Processing in Medicine Conf., pp. 263–274 (1995)

    Google Scholar 

  16. Conners, R.W., Harlow, C.A.: A theoretical comparison of texture algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2(3), 204–222 (1980)

    Article  MATH  Google Scholar 

  17. Coselmon, M.M., Balter, J.M., McShan, D.L., Kessler, Marc L.: Mutual information based CT registration of the lung at exhale and inhale breathing states using thin-plate splines. Med. Phys. 31(11), 2942–2948 (2004)

    Article  Google Scholar 

  18. Crathorne, A.R.: Calculation of the correlation ratio. J. Am. Stat. Assoc. 18(139), 394–396 (1922)

    Article  Google Scholar 

  19. Cvejic, N., Canagarajah, C.N., Bull, D.R.: Information fusion metric based on mutual information and Tsallis entropy. Electron. Lett. 42(11), 626–627 (2006)

    Article  Google Scholar 

  20. D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P.: A viscous fluid model for multimodal non-rigid image registration using mutual information. Med. Image Anal. 7, 565–575 (2003)

    Article  Google Scholar 

  21. De Castro, E., Morandi, C.: Registration of translated and rotated images using finite Fourier transforms. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 700–703 (1987)

    Article  Google Scholar 

  22. dos Santos, R.J.V.: Generalization of Shannon’s theorem of Tsallis entropy. J. Math. Phys. 38(8), 4104–4107 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  23. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn., p. 187. Wiley-Interscience, New York (2001)

    MATH  Google Scholar 

  24. Duncan, T.E.: On the calculation of mutual information. SIAM Journal. Appl. Math. 19(1), 215–220 (1970)

    MATH  Google Scholar 

  25. Evangelidis, G.D., Psarakis, E.Z.: Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1858–1865 (2008)

    Article  Google Scholar 

  26. Fitch, A.J., Kadyrov, A., Christmas, W.J., Kittler, J.: Orientation correlation. In: British Machine Vision Conf., vol. 1, pp. 133–142 (2002)

    Google Scholar 

  27. Fredricks, G.A., Nelsen, R.B.: On the relationship between Spearman’s rho and Kendall’s tau for pairs of continuous random variables. J. Stat. Plan. Inference 137, 2143–2150 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  28. Gao, Z., Gu, B., Lin, J.: Monomodal image registration using mutual information based methods. Image Vis. Comput. 26, 164–173 (2008)

    Article  Google Scholar 

  29. Gauthier, T.D.: Detecting trends using Spearman’s rank correlation, coefficient. Environ. Forensics 2, 359–362 (2001)

    Article  Google Scholar 

  30. Gel’fand, I.M., Yaglom, A.M.: Calculation of the amount of information about a random function contained in another such function. Am. Math. Soc. Trans. 2(12), 199–246 (1959)

    MathSciNet  Google Scholar 

  31. Gibbons, J.D.: Nonparametric Methods for Quantitative Analysis, 2nd edn., p. 298. American Science Press, Columbus (1985)

    MATH  Google Scholar 

  32. Gideon, R.A., Hollister, R.A.: A rank correlation coefficient. J. Am. Stat. Assoc. 82(398), 656–666 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  33. Gilpin, A.R.: Table for conversion of Kendall’s tau and Spearman’s rho within the context of measures of magnitude of effect for meta-analysis. Educ. Psychol. Meas. 53, 87–92 (1993)

    Article  Google Scholar 

  34. Goshtasby, A., Satter, M.: An adaptive window mechanism for image smoothing. Comput. Vis. Image Underst. 111, 155–169 (2008)

    Article  Google Scholar 

  35. Goshtasby, A., Gage, S., Bartholic, J.: A two-stage cross-correlation approach to template matching. IEEE Trans. Pattern Anal. Mach. Intell. 6(3), 374–378 (1984)

    Article  Google Scholar 

  36. Harter, H.L.: Nonuniqueness of least absolute values regression. Commun. Stat. Theor. Math. A6(9), 829–838 (1977)

    Article  MATH  Google Scholar 

  37. Hill, D.L.G., Hawkes, D.J., Harrison, N.A., Ruff, C.F.: A strategy for automated multimodality image registration incorporating anatomical knowledge and image characteristics. In: Proc. 13th Int’l Conf. Information Processing in Medical Imaging, pp. 182–196 (1993)

    Chapter  Google Scholar 

  38. Huntington, E.V.: Mathematics and statistics, with an elementary account of the correlation coefficient and the correlation ratio. Am. Math. Mon. 26(10), 421–435 (1919)

    Article  MathSciNet  MATH  Google Scholar 

  39. Jizba, P., Arimitsu, T.: Observability of Rényi entropy. Phys. Rev. E 69, 026128 (2004), pp. 1–12

    Article  MathSciNet  Google Scholar 

  40. Kaneko, S., Murase, I., Igarashi, S.: Robust image registration by increment sign correlation. Pattern Recognit. 35(10), 2223–2234 (2002)

    Article  MATH  Google Scholar 

  41. Kendall, M.G.: A new measure of rank correlation. Biometrika 30, 81–93 (1938)

    MathSciNet  MATH  Google Scholar 

  42. Kendall, M.G.: Rank Correlation Methods, 3rd edn., p. 12. Charles Birchall and Sons, Liverpool (1962)

    Google Scholar 

  43. Klein, S., Staring, M., Pluim, J.P.W.: Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE Trans. Image Process. 16(12), 2879–2890 (2007)

    Article  MathSciNet  Google Scholar 

  44. Kories, R., Zimmerman, G.: A versatile method for the estimation of displacement vector fields from image sequences. In: Workshop on Motion Representation and Analysis, pp. 101–107 (1986)

    Google Scholar 

  45. Kotlyar, M., Fuhrman, S., Ableson, A., Somogyi, R.: Spearman correlation identifies statistically significant gene expression clusters in spinal cord development and injury. Neurochem. Res. 27(10), 1133–1140 (2002)

    Article  Google Scholar 

  46. Krotosky, S.J., Trivedi, M.M.: Mutual information based registration of multimodal stereo video for person tracking. Comput. Vis. Image Underst. 106, 270–287 (2007)

    Article  Google Scholar 

  47. Kruskal, W.: Ordinal measures of association. J. Am. Stat. Assoc. 53, 814–861 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  48. Kuglin, C.D., Hines, D.C.: The phase correlation image alignment method. In: Proc. Int’l Conf. Cybernetics and Society, pp. 163–165 (1975)

    Google Scholar 

  49. Lan, Z.-D., Mohr, R.: Robust matching by partial correlation. In: Proc. 6th British Machine Vision Conf., pp. 651–660 (1995)

    Google Scholar 

  50. Lau, Y.H., Braun, M., Hutton, B.F.: Non-rigid image registration using a median-filtered coarse-to-fine displacement field and a symmetric correlation ratio. Phys. Med. Biol. 46, 1297–1319 (2001)

    Article  Google Scholar 

  51. Likar, B., Pernuš, F.: A hierarchical approach to elastic registration based on mutual information. Image Vis. Comput. 19, 33–44 (2001)

    Article  Google Scholar 

  52. Liu, L., Jiang, T., Yang, J., Zhu, C.: Fingerprint registration by maximization of mutual information. IEEE Trans. Image Process. 15(5), 1100–1110 (2006)

    Article  Google Scholar 

  53. Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)

    Article  Google Scholar 

  54. Maes, F., Vandermeulen, D., Suetens, P.: Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Med. Image Anal. 3(4), 373–386 (1999)

    Article  Google Scholar 

  55. Maes, F., Vandermeulen, D., Suetens, P.: Medical image registration using mutual information. Proc. IEEE 91(10), 1699–1722 (2003)

    Article  Google Scholar 

  56. Martin, S., Morison, G., Nailon, W., Durrani, T.: Fast and accurate image registration using Tsallis entropy and simultaneous perturbation stochastic approximation. Electron. Lett. 40(10), 595–597 (2004)

    Article  Google Scholar 

  57. Matthäus, L., Trillenberg, P., Fadini, T., Finke, M., Schweikard, A.: Brain mapping with transcranial magnetic stimulation using a refined correlation ratio and Kendall’s τ. Stat. Med. 27, 5252–5270 (2008)

    Article  MathSciNet  Google Scholar 

  58. McLaughlin, P.W., Narayana, V., Kessler, M., McShan, D., Troyer, S., Marsh, L., Hixson, G., Roberson, P.L.: The use of mutual information in registration of CT and MRI datasets post permanent implant. Brachytherapy 3, 61–70 (2004)

    Article  Google Scholar 

  59. Meer, P., Park, R.H., Cho, K.: Multiresolution adaptive image smoothing. CVGIP, Graph. Models Image Process. 56(2), 140–148 (1994)

    Article  Google Scholar 

  60. Mellor, M., Brady, M.: Phase mutual information as a similarity measure for registration. Med. Image Anal. 9, 330–343 (2005)

    Article  Google Scholar 

  61. Milko, S., Melvaer, E.L., Samset, E., Kadir, T.: Evaluation of the bivariate correlation ratio similarity measure metric for rigid registration of US/MR images of the liver. Int. J. Comput. Assisted Radiol. Surg. 4, 147–155 (2009)

    Article  Google Scholar 

  62. Muselet, D., Trémeau, A.: Rank correlation as illumination invariant descriptor for color object recognition. In: Proc. 15th Int’l Conf. Image Processing, pp. 157–160 (2008)

    Google Scholar 

  63. Nalpantidis, L., Sirakoulis, G.Ch., Gasteratos, A.: A dense stereo correspondence algorithm for hardware implementation with enhanced disparity selection. In: Lecture Notes in Computer Science, vol. 5138, pp. 365–370. Springer, Berlin (2008)

    Google Scholar 

  64. Okutomi, M., Kanade, T.: A locally adaptive window for signal matching. Int. J. Comput. Vis. 7(2), 143–162 (1992)

    Article  Google Scholar 

  65. Pearson, K.: Contributions to the mathematical theory of evolution, III, Regression, heredity, and panmixia. Philos. Trans. R. Soc. Lond. Ser. A 187, 253–318 (1896)

    Article  MATH  Google Scholar 

  66. Pearson, K.: Mathematical contributions to the theory of evolution, XIV, on the general theory of skew correlation and non-linear regression. In: Drapers’ Company Research Memoirs, Biometric Series, II. Dulau and Co., London (1905), 54 p.

    Google Scholar 

  67. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  68. Pluim, J.P.W., Antoine, J.B., Viergever, M.: Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Med. Imaging 19(8), 809–814 (2000)

    Article  Google Scholar 

  69. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: f-information measures in medical image registration. IEEE Trans. Med. Imaging 23(12), 1506–1518 (2004)

    Article  Google Scholar 

  70. Raggio, G.A.: Properties of q-entropies. J. Math. Phys. 36(9), 4785–4791 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  71. Rangarajan, A., Chui, H., Duncan, J.: Rigid point feature registration using mutual information. Med. Image Anal. 3(4), 425–440 (1999)

    Article  Google Scholar 

  72. Reddy, B.S., Chatterji, B.: An FFT-based technique for translation, rotation and scale invariant image registration. IEEE Trans. Image Process. 5(8), 1266–1271 (1996)

    Article  Google Scholar 

  73. Rényi, A.: On measures of entropy and information. In: Proc. Fourth Berkeley Symposium on Mathematical Statistics Probability, vol. 1, pp. 547–561. University of California Press, Berkeley (1961). Also available in Selected Papers of Alfréd Rényi 2, 525–580 (1976)

    Google Scholar 

  74. Rényi, A.: Probability Theory. American Elsevier Publishing, North Holland, Amsterdam (1970)

    Google Scholar 

  75. Roche, A., Malandain, G., Pennec, X., Ayache, N.: The correlation ratio as a new similarity measure for multimodal image registration. Lect. Notes Comput. Sci. 1496, 1115–1124 (1998). Also, see Multimodal Image Registration by Maximization of the Correlation Ratio, Report No. 3378, Institute de Research en Informatique et en Automatique, Aug. 1998

    Article  Google Scholar 

  76. Roche, A., Pennec, X., Malandain, G., Ayache, N.: Rigid registration of 3-D ultrasound with MR images: A new approach combining intensity and gradient information. IEEE Trans. Med. Imaging 20(10), 1038–1049 (2001)

    Article  Google Scholar 

  77. Rosenfeld, A., Vanderburg, G.J.: Coarse-fine template matching. IEEE Trans. Syst. Man Cybern. 7(2), 104–107 (1977)

    Article  Google Scholar 

  78. Rougon, N.F., Petitjean, C., Preteux, F.: Variational non-rigid image registration using exclusive f-information. In: Proc. Int’l Conf. Image Processing, Los Alamitos, CA, pp. 703–706 (2003)

    Google Scholar 

  79. Rousseeuw, P.J.: Least median of squares regression. J. Am. Stat. Assoc. 79(388), 871–880 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  80. Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. Wiley, New York (1987)

    Book  MATH  Google Scholar 

  81. Rueckert, D., Clarkson, M.J., Hill, D.L., Hawkes, D.J.: Non-rigid registration using high-order mutual information. In: Proc. SPIE Image Processing: Medical Imaging, vol. 3979, pp. 438–447 (2000)

    Google Scholar 

  82. Shannon, C.E.: The mathematical theory of communication. In: Shannon, C.E., Weaver, W. (eds.) The Mathematical Theory of Communication, pp. 29–125. University of Illinois Press, Urbana (1949), reprint 1998

    Google Scholar 

  83. Shapiro, L.G., Stockman, G.C.: Computer Vision, p. 219. Prentice Hall, Upper Saddle River (2001)

    Google Scholar 

  84. Shieh, G.S.: A weighted Kendall’s tau statistic. Stat. Probab. Lett. 39, 17–24 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  85. Skouson, M.B., Guo, Q., Liang, Z.-P.: A bound on mutual information for image registration. IEEE Trans. Med. Imaging 20(8), 843–846 (2001)

    Article  Google Scholar 

  86. Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15(1), 72–101 (1904)

    Article  Google Scholar 

  87. Stone, H.S.: Fast correlation and phase correlation. In: Le Moigne, J., Netanyahu, N.S., Eastman, R.D. (eds.) Image Registration for Remote Sensing, pp. 79–111. Cambridge University Press, Cambridge (2011)

    Chapter  Google Scholar 

  88. Studholme, C., Hill, D.L.G., Hawkes, D.J.: Automated 3D registration of truncated MR and CT images of the head. In: Proc. British Machine Vision Conf., pp. 27–36 (1995)

    Google Scholar 

  89. Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit. 32, 71–86 (1999)

    Article  Google Scholar 

  90. Tao, G., He, R., Datta, S., Narayana, P.A.: Symmetric inverse consistent nonlinear registration driven by mutual information. Comput. Methods Programs Biomed. 95, 105–115 (2009)

    Article  Google Scholar 

  91. Tedeschi, W., Müller, H.-P., de Araujo, D.B., Santos, A.C., Neves, U.P.C., Erné, S.N., Baffa, O.: Generalized mutual information fMRI analysis: A study of the Tsallis q parameter. Physica A 344, 705–711 (2004)

    Article  Google Scholar 

  92. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, New York (2009), pp. 602, 605, 606

    Google Scholar 

  93. Thévenaz, P., Unser, M.: Optimization of mutual information for multiresolution image registration. IEEE Trans. Image Process. 9(12), 2083–2099 (2000)

    Article  MATH  Google Scholar 

  94. Tsallis, C.: Possible generalization of Boltzmann-Gibbs statistics. J. Stat. Phys. 52, 479–487 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  95. Vajda, I.: Theory of Statistical Evidence and Information, p. 309. Kluwer Academic, Dordrecht (1989)

    Google Scholar 

  96. van Hecke, W., Leemans, A., D’Angostino, E., De Backer, S., Vandervliet, E., Parizel, P.M., Sijbers, J.: Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information. IEEE Trans. Med. Imaging 26(11), 1598–1612 (2007)

    Article  Google Scholar 

  97. Vanderburg, G.J., Rosenfeld, A.: Two-stage template matching. IEEE Trans. Comput. 26, 384–393 (1977)

    Article  Google Scholar 

  98. Venot, A., Leclerc, V.: Automated correction of patient motion and gray values prior to subtraction in digitized angiography. IEEE Trans. Med. Imaging 3, 179–186 (1984)

    Article  Google Scholar 

  99. Venot, A., Lebruchec, J.F., Golmard, J.L., Roucayrol, J.C.: An automated method for the normalization of scintigraphic images. J. Nucl. Med. 24, 529–531 (1983)

    Google Scholar 

  100. Venot, A., Devaux, J.Y., Herbin, M., Lebruchec, J.F., Dubertret, L., Raulo, Y., Roucayrol, J.C.: An automated system for the registration and comparison of photographic images in medicine. IEEE Trans. Med. Imaging 7(4), 298–303 (1988)

    Article  Google Scholar 

  101. Viola, P., Wells, W.M. III: Alignment by maximization of mutual information. Int. J. Comput. Vis. 24(2), 137–154 (1997)

    Article  Google Scholar 

  102. Wachowiak, M.P., Smolikova, R., Tourassi, G.D., Elmaghraby, A.S.: Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration. In: Medical Imaging Conf., Proc. SPIE, vol. 5032, San Diego, CA, pp. 1090–1100 (2003)

    Google Scholar 

  103. Walimbe, V., Zagrodsky, V., Raja, S., Jaber, W.A., DiFilippo, F.P., Garcia, M.J., Brunken, R.C., Thomas, J.D., Shekhar, R.: Mutual information-based multimodality registration of cardiac ultrasound and SPECT images: a preliminary investigation. Int. J. Card. Imaging 19, 483–494 (2003)

    Article  Google Scholar 

  104. Wells, W.M. III, Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35–51 (1996)

    Article  Google Scholar 

  105. Wong, K.K., Yang, E.S., Wu, E.X., Tse, H.-F., Wong, S.T.: First-pass myocardial perfusion image registration by minimization of normalized mutual information. J. Magn. Reson. Imaging 27, 529–537 (2008)

    Article  Google Scholar 

  106. Woods, R.P., Cherry, S.R., Mazziotta, J.C.: Rapid automated algorithm for aligning and reslicing PET images. J. Comput. Assist. Tomogr. 16, 620–633 (1992)

    Article  Google Scholar 

  107. Woods, R.P., Mazziotta, J.C., Cherry, S.R.: MRI-PET registration with automated algorithm. J. Comput. Assist. Tomogr. 17(4), 536–546 (1993)

    Article  Google Scholar 

  108. Xu, P., Yao, D.: A study on medical image registration by mutual information with pyramid data structure. Comput. Biol. Med. 37, 320–327 (2007)

    Article  Google Scholar 

  109. Yokoi, T., Soma, T., Shinohara, H., Matsuda, H.: Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images. Ann. Nucl. Med. 18(8), 659–667 (2004)

    Article  Google Scholar 

  110. Yue, S., Pilon, P., Cavadias, G.: Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J. Hydrol. 259, 254–271 (2002)

    Article  Google Scholar 

  111. Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: European Conf. Computer Vision, Stockholm, Sweden, pp. 151–158 (1994)

    Google Scholar 

  112. Zhu, H., Shu, H., Xia, T., Luo, L., Coatrieux, J.L.: Translation and scale invariants of Tchebichef moments. Pattern Recognit. 40, 2530–2542 (2007)

    Article  MATH  Google Scholar 

  113. Zyczkowski, K.: Rényi extrapolation of Shannon entropy. Open Syst. Inf. Dyn. 10, 297–310 (2003)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Computer Science and Engineering, 303 Russ Engineering Center, Wright State University, Dayton, OH, USA

    Prof. A. Ardeshir Goshtasby

Authors
  1. Prof. A. Ardeshir Goshtasby
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Ardeshir Goshtasby .

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Limited

About this chapter

Cite this chapter

Goshtasby, A.A. (2012). Similarity and Dissimilarity Measures. In: Image Registration. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2458-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2458-0_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2457-3

  • Online ISBN: 978-1-4471-2458-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation