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.
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References
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).
Mani, V.R.S and Rivazhagan, S. Survey of Medical Image Registration, Journal of Biomedical Engineering and Technology, 1 (2), 8–25 (2013).
Oliveira, F. P., Tavares, J.M.R. Medical image registration: a review, Computer methods in biomechanics and biomedical engineering, 17 (2), 73–93 (2014).
Acharyya, M., Kundu, M.K., An adaptive approach to unsupervised texture segmentation using M-band wavelet tranform, Signal Processing, 81(7), 1337–1356, (2001).
Starck, J.L., Candes, E.J., Donoho, D.L., The curvelet transform for image denoising, IEEE Transactions on Image Processing 11, 670–684 (2002).
Candes, E.J., Donoho, D., Continuous curvelet transform: II. Discretization and frames, Applied and Computational Harmonic Analysis 19, 198–222 (2005).
Candes, E.J., Donoho, D., Ridgelets: a key to higher-dimensional intermittency, Philosophical Transactions: Mathematical, Physical and Engineering Sciences 357 (1760) 2495–2509 (1999).
Do, M.N., Vetterli, M., The finite Ridgelet transform for image representation, IEEE Transactions on Image Processing 12 (1), 16–28 (2003).
Do, M.N., Vetterli, M., The contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on Image Processing 14 (12), 2091–2106 (2005).
Pennec, E. Le, Mallat, S.: Sparse geometric image representations with bandelets, IEEE Transactions on Image Processing 14 (4), 423–438 (2005).
Flusser, J., Sroubek, F., Zitova, B., Image Fusion:Principles, Methods, Lecture Notes Tutorial EUSIPCO (2007).
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).
Alam, Md., Howlader, T., Rahman S.M.M., Entropy-based image registration method using the curvelet transform, SIViP, 8, 491505, (2014).
Xu, J., Yang, L., Wu, D., A new transform for image processing, J. Vis. Commun. Image Representation, 21, 627–639 (2010).
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).
Das, S., Kundu, M. K., Medical image fusion using ripplet transform type-1, Progress in electromagnetic research B, 30, 355–370, (2011).
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Pradhan, S., Patra, D., Singh, A. (2017). Image Registration of Medical Images Using Ripplet Transform. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_44
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DOI: https://doi.org/10.1007/978-981-10-2107-7_44
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