Nonrigid Image Registration of Brain MR Images Using Normalized Mutual Information

  • Smita Pradhan
  • Dipti Patra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Registration is an advanced technique which maps two images spatially and can produce an informative image. Intensity-based similarity measures are increasingly used for medical image registration that helps clinicians for faster and more effective diagnosis. Recently, mutual information (MI)-based image registration techniques have become popular for multimodal brain images. In this chapter, normalized mutual information (NMI) method has been employed for brain MR image registration. Here, the intensity patterns are encoded through similarity measure technique. NMI is an entropy-based measure that is invariant to the overlapped regions of the two images. To take care of the deformations, transformation of the floating image is performed using B-spline method. NMI-based image registration is performed for similarity measure between the reference and floating image. Optimal evaluation of joint probability distribution of the two images is performed using parzen window interpolation method. The hierarchical approach to nonrigid registration based on NMI is presented in which the images are locally registered and nonrigidly interpolated. The proposed method for nonrigid registration is validated with both clinical and artificial brain MR images. The obtained results show that the images could be successfully registered with 95 % of correctness.


Medical image registration Mutual information Normalized mutual information B-spline method 


  1. 1.
    Brown, L.G.R.: A survey of image registration techniques, ACM Comput. Surv. 24(4), 325–376 (1992)Google Scholar
  2. 2.
    Zitova, Barbara, Flusser, Jan: Image registration methods: a survey. Image Vis. Comput. 21, 977–1000 (2003)CrossRefGoogle Scholar
  3. 3.
    Pluim, J., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imag. 22(8), 986–1004 (2003)CrossRefGoogle Scholar
  4. 4.
    Viola, P., Wells, W.M.: Alignment by maximization of mutual information. In: ICCV ’95 Proceedings of the Fifth International Conference on Computer Vision, IEEE Computer Society (1995)Google Scholar
  5. 5.
    Pluim, J., Maintz, J., Viergever, M.: Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Med. Imag. 19(8), 809–814 (2000)CrossRefGoogle Scholar
  6. 6.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Interpolation artefacts in mutual information-based image registration. Comput. Vis. Image Understand. 77(2), 211–232 (2000)CrossRefGoogle Scholar
  7. 7.
    Likar, B., Pernu, F.: A hierarchical approach to elastic registration based on mutual information. Image Vis. Comput. 19(1–2), 33–44 (2001)CrossRefGoogle Scholar
  8. 8.
    Chen, H.M., Varshney, P.K.: Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation. IEEE Trans. Med. Imag. 22(9), 1111–1119 (2003)CrossRefGoogle Scholar
  9. 9.
    Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J.V., Hammers, A.: Diffeomorphic registration using b-splines. Medical Image Computing and Computer-Assisted Intervention, Lecture Notes Computer Science, 4191, 702–709. Springer, New york (2006)Google Scholar
  10. 10.
    Studholme, C., Drapaca, C., Iordanova, B., Cardenas, V.: Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change. IEEE Trans. Med. Imag. 25(5), 626–639 (2006)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Andronache, A., Siebenthal, M.v., Szekely, G., Cattin, P.: Nonrigid registration of multimodal images using both mutual information and cross-correlation. Med. Image Anal. 12, 3–15 (2008)Google Scholar
  13. 13.
    Khader, M., Hamza, A.B., An entropy-based technique for nonrigid medical image alignment. In: Proceedings of the 14th International Workshop Combinatorial Image, Analysis, pp. 444–455 May 2011Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.IPCV Laboratory, Department of Electrical EngineeringNITRourkelaIndia
  2. 2.Deptartment of Electrical EngineeringNITRourkelaIndia

Personalised recommendations