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Image Registration of Medical Images Using Ripplet Transform

  • Smita PradhanEmail author
  • Dipti Patra
  • Ajay Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

For image fusion of geometrically distorted images, registration is the prerequisite step. Intensity-based image registration methods are preferred due to higher accuracy than that of feature-based methods. But, perfect registered image using intensity based method leads towards improvements in computational complexity. Conventional transform like wavelet transform based image registration reduces the computational complexity, but suffers from discontinuities such as curved edges in the medical images. In this paper, a new registration algorithm is proposed that uses the approximate-level coefficients of the ripplet transform, which allows arbitrary support and degree as compared to curvelet transform. The entropy-based objective function is developed for registration using ripplet coefficients of the images. The computations are carried out with 6 sets of CT and MRI brain images to validate the performance of the proposed registration technique. The quantitative approach such as standard deviation, mutual information, peak signal to noise ratio and root mean square error are used as performance measure.

Keywords

Image registration Ripplet transform Standard deviation Mutual information Root mean square error Peak signal noise ratio 

References

  1. 1.
    Pradhan, S., Patra, D. RMI based nonrigid image registration using BF-QPSO optimization and P-spline, AEU-International Journal of Electronics and Communications, 69 (3), 609–621 (2015).Google Scholar
  2. 2.
    Mani, V.R.S and Rivazhagan, S. Survey of Medical Image Registration, Journal of Biomedical Engineering and Technology, 1 (2), 8–25 (2013).Google Scholar
  3. 3.
    Oliveira, F. P., Tavares, J.M.R. Medical image registration: a review, Computer methods in biomechanics and biomedical engineering, 17 (2), 73–93 (2014).Google Scholar
  4. 4.
    Acharyya, M., Kundu, M.K., An adaptive approach to unsupervised texture segmentation using M-band wavelet tranform, Signal Processing, 81(7), 1337–1356, (2001).Google Scholar
  5. 5.
    Starck, J.L., Candes, E.J., Donoho, D.L., The curvelet transform for image denoising, IEEE Transactions on Image Processing 11, 670–684 (2002).Google Scholar
  6. 6.
    Candes, E.J., Donoho, D., Continuous curvelet transform: II. Discretization and frames, Applied and Computational Harmonic Analysis 19, 198–222 (2005).Google Scholar
  7. 7.
    Candes, E.J., Donoho, D., Ridgelets: a key to higher-dimensional intermittency, Philosophical Transactions: Mathematical, Physical and Engineering Sciences 357 (1760) 2495–2509 (1999).Google Scholar
  8. 8.
    Do, M.N., Vetterli, M., The finite Ridgelet transform for image representation, IEEE Transactions on Image Processing 12 (1), 16–28 (2003).Google Scholar
  9. 9.
    Do, M.N., Vetterli, M., The contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on Image Processing 14 (12), 2091–2106 (2005).Google Scholar
  10. 10.
    Pennec, E. Le, Mallat, S.: Sparse geometric image representations with bandelets, IEEE Transactions on Image Processing 14 (4), 423–438 (2005).Google Scholar
  11. 11.
    Flusser, J., Sroubek, F., Zitova, B., Image Fusion:Principles, Methods, Lecture Notes Tutorial EUSIPCO (2007).Google Scholar
  12. 12.
    Manu, V. T., Simon P., A novel statistical fusion rule for image fusion and its comparison in non-subsampled contourlet transform domain and wavelet domain, The International Journal of Multimedia and Its Applications, (IJMA), 4 (2), 69–87 (2012).Google Scholar
  13. 13.
    Alam, Md., Howlader, T., Rahman S.M.M., Entropy-based image registration method using the curvelet transform, SIViP, 8, 491505, (2014).Google Scholar
  14. 14.
    Xu, J., Yang, L., Wu, D., A new transform for image processing, J. Vis. Commun. Image Representation, 21, 627–639 (2010).Google Scholar
  15. 15.
    Chowdhury, M., Das, S., Kundu, M. K., CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique, Electronic Letters on Computer Vision and Image Analysis 11(1), 1–13, (2012).Google Scholar
  16. 16.
    Das, S., Kundu, M. K., Medical image fusion using ripplet transform type-1, Progress in electromagnetic research B, 30, 355–370, (2011).Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.IPCV Lab, Department of Electrical EngineeringNational Institute of TechnologyRourkelaIndia

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