Image Registration Based on Sum of Square Difference Cost Function

Khosravi, J.; Shams Esfand Abadi, Mohammad; Ebrahimpour, R.

doi:10.22061/jecei.2019.5544.235

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	Image Registration Based on Sum of Square Difference Cost Function
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 13، دوره 6، شماره 2، مهر 2018، صفحه 273-281 اصل مقاله (1.1 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2019.5544.235
نویسندگان
J. Khosravi¹؛ Mohammad Shams Esfand Abadi²؛ R. Ebrahimpour^* ³
¹Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, P.O.Box:16785-163, Tehran, Iran.
²Electrical Engineering Department, Shahid Rajaee Teacher Training University, Tehran, Iran
³SRTTU
تاریخ دریافت: 02 مهر 1396، تاریخ بازنگری: 18 دی 1396، تاریخ پذیرش: 25 اردیبهشت 1397
چکیده
Background and Objectives: There are numerous applications for image registration (IR). The main purpose of the IR is to find a map between two different situation images. In this way, the main objective is to find this map to reconstruct the target image as optimum as possible. Methods: Needless to say, the IR task is an optimization problem. As the optimization method, although the evolutionary ones are sometimes more effective in escaping the local minima, their speed is not emulated the mathematical ones at all. In this paper, we employed a mathematical framework based on the Newton method. This framework is suitable for any efficient cost function. Yet we used the sum of square difference (SSD). We also provided an effective strategy in order to avoid sticking in the local minima. Results: The proposed newton method with SSD as a cost function expresses more decent speed and accuracy in comparison to Gradient descent and genetic algorithms methods based on presented criteria. By considering SSD as the model cost function, the proposed method is able to introduce, respectively, accurate and fast registration method which could be exploited by the relevant applications. Simulation results indicate the effectiveness of the proposed model. Conclusion: The proposed innovative method based on the Newton optimization technique on separate cost functions is able to outperform regular Gradient descent and genetic algorithms. The presented framework is not based on any specific cost function, so any innovative cost functions could be effectively employed by our approach. Whether the objective is to reach accurate or fast results, the proposed method could be investigated accordingly. ====================================================================================================== Copyrights ©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers. ======================================================================================================
کلیدواژه‌ها
Image registration؛ Sum of Square Difference؛ Root Mean Square Error؛ Mutual Information

مراجع
[1] J. Fan, X. Cao, P.-T. Yap, D. Shen, “BIRNet: Brain image registration using dual-supervised fully convolutional networks,” Med. Image Anal., 54: 193–206, 2019. [2] M. Moradi, M. Sadeghi, “Combining and steganography of 3-D face textures,” J. Electr. Comput. Eng. Innov., 5(2): 93–100, 2017. [3] R. Nain, N. Kumar, “Medical image registration by GSA optimized matching algorithm,” Int. J. Curr. Eng. Technol., 6(2): 472–476, 2016. [4] Z. Xu et al., “Rigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration,” Magn. Reson. Imaging, 57: 303–312, 2019. [5] G. C. S. Ruppert et al., “Medical image registration based on watershed transform from greyscale marker and multi-scale parameter search,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 5(2): 138–156, 2017. [6] R. Panda, S. Agrawal, M. Sahoo, R. Nayak, “A novel evolutionary rigid body docking algorithm for medical image registration,” Swarm EComput., 33): 108–118, 2017. [7] B. Haghighi, N. D. Ellingwood, Y. Yin, E. A. Hoffman, C. L. Lin, “A GPU-based symmetric non-rigid image registration method in human lung,” Med. Biol. Eng. Comput., 56(3): 355–371, 2018. [8] A. Sotiras, C. Davatzikos, N. Paragios, “Deformable medical image registration: a survey,” IEEE Trans. Med. Imaging, 32(7): 1153–1190, 2013. [9] H. Yu et al., “Learning 3D non-rigid deformation based on an unsupervised deep learning for PET/CT image registration,” presented at The Biomedical Applications in Molecular, Structural, and Functional Imaging, California, United States, 2019. [10] A. Valsecchi, S. Damas, J. Santamaria, “Evolutionary intensity-based medical image registration: a review,” Curr. Med. Imaging Rev., 9(4): 283–297, 2014. [11] E. Castillo, “Quadratic penalty method for intensity‐based deformable image registration and 4DCT lung motion recovery,” Med. Phys., 46(5): 2194–2203, 2019. [12] L. Han, H. Dong, J. R. McClelland, L. Han, D. J. Hawkes, D. C. Barratt, “A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs,” Med. Image Anal., 39: 87–100, 2017. [13] O. Lobachev, C. Ulrich, B. S. Steiniger, V. Wilhelmi, V. Stachniss, M. Guthe, “Feature-based multi-resolution registration of immunostained serial sections,” Med. Image Anal., 35: 288–302, 2017. [14] J. Li, Q. Hu, M. Ai, “Robust feature matching for remote sensing image registration based on LQ-estimator,” IEEE Geosci. Remote Sens. Lett., 13(12): 1989–1993, 2016. [15] S. Klein, J. P. W. Pluim, M. Staring, M. A. Viergever, “Adaptive stochastic gradient descent optimisation for image registration,” Int. J. Comput. Vis., 81(3): 227–239, 2009. [16] Y. Wu, W. Ma, Q. Miao, S. Wang, “Multimodal continuous ant colony optimization for multisensor remote sensing image registration with local search,” Swarm and Evolutionary Computation, 47): 89-95, 2019. [17] H. Ismkhan, “Effective heuristics for ant colony optimization to handle large-scale problems,” Swarm EComput., 32): 140–149, 2017. [18] F. Ayatollahi, S. B. Shokouhi, A. Ayatollahi, “A new hybrid particle swarm optimization for multimodal brain image registration,” J. Biomed. Sci. Eng., 05(04): 153–161, 2012. [19] I. Behravan, S. Zahiri, S. M. Razavi, "Clustering a Big Mobility Dataset Using an Automatic Swarm Intelligence-Based Clustering Method," J. Electr. Comput. Eng. Innov., 6(2): 243-261, 2019. [20] S. Elhag, A. Fernández, A. Bawakid, S. Alshomrani, F. Herrera, “On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems,” Expert Syst. Appl., 42(1): 193–202, 2015. [21] A. Mang, G. Biros, “A semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration,” SIAM J. Sci. Comput., 39(6): B1064–B1101, 2017. [22] S. Ying, D. Li, B. Xiao, Y. Peng, S. Du, M. Xu, “Nonlinear image registration with bidirectional metric and reciprocal regularization,” PLoS One, 12(2): 1–19, 2017. [23] K. Chen, G. N. Grapiglia, J. Yuan, D. Zhang, “Improved optimization methods for image registration problems,” Numer. Algorithms, 80(2): 305–336, 2019. [24] E. Ferrante, N. Paragios, “Slice-to-volume medical image registration: A survey,” Med. Image Anal., 39): 101–123, 2017. [25] S. Klein, M. Staring, J. P. W. Pluim, “Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines,” IEEE Trans. Image Process., 16(12): 2879–2890, 2007. [26] S. Etemadi, M. Saadatmand-Tarzjan, M. Shamirzaei, J. Khosravi, “An efficient 3D gradient-based algorithm for medical image registration using correlation-coefficient maximization,” in Proc. 4^th International Conference on Computer and Knowledge Engineering (ICCKE): 663–668, 2014. [27] J. Le Moigne, W. J. Campbell, R. F. Cromp, “An automated parallel image registration technique based on the correlation of wavelet features,” IEEE Trans. Geosci. Remote Sens., 40(8): 1849–1864, 2002. [28] K. Yang, A. Pan, Y. Yang, S. Zhang, S. Ong, H. Tang, “Remote sensing image registration using multiple image features,” Remote Sens., 9(6, p. 581, 2017. [29] Y. Qiao, B. P. F. Lelieveldt, M. Staring, “An efficient preconditioner for stochastic gradient descent optimization of image registration,” IEEE Trans. Med. Imaging, 9: 10–19, 2019. [30] J. Zhang, G. Chen, Z. Jia, “An image stitching algorithm based on histogram matching and sift algorithm,” Int. J. Pattern Recognit. Artif. Intell., 31(04): 1754006-2–14, 2017. [31] H. Jagadish, J. Prakash, “Adaptive Markov random field model for area based image registration and change detection,” Int. J. Appl. or Innov. Eng. Manag., 6(4): 50–58, 2017. [32] Y. Li, C. Chen, Fei Yang, J. Huang, “Deep sparse representation for robust image registration,” in Proc. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 4894–4901, 2015. [33] F. Maes, D. Vandermeulen, P. Suetens, “Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information,” Med. Image Anal., 3(4): 373–386, 1999. [34] E. Vural, P. Frossard, “Analysis of descent-based image registration,” SIAM J. Imaging Sci., 6(4): 2310–2349, 2013. [35] Y. Qiao, B. van Lew, B. P. F. Lelieveldt, M. Staring, “Fast automatic step size estimation for gradient descent optimization of image registration,” IEEE Trans. Med. Imaging, 35(2): 391–403, 2016. [36] M. Unser, P. Thevenaz, “Optimization of mutual information for multiresolution image registration,” IEEE Trans. Image Process., 9(12): 2083–2099, 2000. [37]J. Dong, K. Lu, J. Xue, S. Dai, R. Zhai, W. Pan, “Accelerated nonrigid image registration using improved Levenberg–Marquardt method,” Inf. Sci. (Ny)., 423): 66–79, 2018.
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Image Registration Based on Sum of Square Difference Cost Function