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Quantum implementation of image registration

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Abstract

A quantum implementation of image registration is proposed based on a quantum version of Powell’s conjugate direction method in this study. Quantum Powell’s method can find the minimum parameters of similarity measurement between the quantum fixed and moving images in the solution space when the quantum moving image performs geometric transformation. By combining quantum computing units and basic quantum gates, a series of quantum circuits are designed to implement the quantum image registration method which contains quantum image similarity measure, quantum one-dimensional search algorithm, quantum updating search direction array and quantum termination condition of Powell’s iteration. To improve the search efficiency of the algorithm, a quantum version of golden section search method is proposed. The simulation experiments verify the validity of quantum Powell’s image registration method.

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References

  1. Wang, Z., Xu, M., Zhang, Y.: Review of quantum image processing. Arch. Comput. Methods Eng. 29(2), 737–761 (2021). https://doi.org/10.1007/s11831-021-09599-2

    Article  MathSciNet  Google Scholar 

  2. Yan, F., Iliyasu, A.M., Le, P.Q.: Quantum image processing: A review of advances in its security technologies. Int. J. Quantum Inf. 15(03), 1730001 (2017). https://doi.org/10.1142/s0219749917300017

    Article  MathSciNet  MATH  Google Scholar 

  3. Grover, L.K.: Synthesis of quantum superpositions by quantum computation. Phys. Rev. Lett. 85(6), 1334–1337 (2000). https://doi.org/10.1103/physrevlett.85.1334

    Article  ADS  Google Scholar 

  4. Horodecki, R., Horodecki, P., Horodecki, M., Horodecki, K.: Quantum entanglement. Rev. Mod. Phys. 81(2), 865–942 (2009). https://doi.org/10.1103/revmodphys.81.865

    Article  MathSciNet  MATH  ADS  Google Scholar 

  5. Guanlei, X., Xiaogang, X., Xun, W., Xiaotong, W.: A novel quantum image parallel searching algorithm. Optik 209, 164565 (2020). https://doi.org/10.1016/j.ijleo.2020.164565

    Article  ADS  Google Scholar 

  6. Tezuka, H., Nakaji, K., Satoh, T., Yamamoto, N.: Grover search revisited: Application to image pattern matching. Phys. Rev. A 105(3), 032440 (2022). https://doi.org/10.1103/physreva.105.032440

    Article  MathSciNet  ADS  Google Scholar 

  7. Yan, F., Zhao, S., Venegas-Andraca, S.E., Hirota, K.: Implementing bilinear interpolation with quantum images. Digit. Signal Process. 117, 103149 (2021). https://doi.org/10.1016/j.dsp.2021.103149

    Article  Google Scholar 

  8. Dong, H., Lu, D., Li, C.: A novel qutrit representation of quantum image. Quantum Inf. Process. (2022). https://doi.org/10.1007/s11128-022-03450-8

    Article  MathSciNet  MATH  Google Scholar 

  9. Jiang, N., Ji, Z., Wang, J., Lu, X., Zhou, R.: Quantum image histogram statistics. Int. J. Theor. Phys. 59(11), 3533–3548 (2020). https://doi.org/10.1007/s10773-020-04614-x

    Article  MathSciNet  MATH  Google Scholar 

  10. Chetia, R., Boruah, S.M.B., Sahu, P.P.: Quantum image edge detection using improved sobel mask based on NEQR. Quant Inf. Process. (2021). https://doi.org/10.1007/s11128-020-02944-7

    Article  MathSciNet  MATH  Google Scholar 

  11. Gao, Y., Xie, H., Zhang, J., Zhang, H.: A novel quantum image encryption technique based on improved controlled alternated quantum walks and hyperchaotic system. Phys. A Stat. Mech. Appl. (2022). https://doi.org/10.1016/j.physa.2022.127334

    Article  MATH  Google Scholar 

  12. Chen, G., Song, X., Venegas-Andraca, S.E., El-Latif, A.A.A.: QIRHSI: novel quantum image representation based on hsi color space model. Quantum Inf. Process. 21(1), 1–31 (2022). https://doi.org/10.1007/s11128-021-03337-0

    Article  MathSciNet  MATH  ADS  Google Scholar 

  13. Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003). https://doi.org/10.1016/s0262-8856(03)00137-9

    Article  Google Scholar 

  14. Yuan, S., Qing, X., Hang, B., Qu, H.: Quantum color image median filtering in the spatial domain: theory and experiment. Quantum Inf. Process. 21(9), 1–18 (2022). https://doi.org/10.1007/s11128-022-03660-0

    Article  MathSciNet  MATH  ADS  Google Scholar 

  15. Oliveira, F.P.M., Tavares, J.M.R.S.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Eng. 17(2), 73–93 (2012). https://doi.org/10.1080/10255842.2012.670855

    Article  Google Scholar 

  16. Matl, S., Brosig, R., Baust, M., Navab, N., Demirci, S.: Vascular image registration techniques: A living review. Med. Image Anal. 35, 1–17 (2017). https://doi.org/10.1016/j.media.2016.05.005

    Article  Google Scholar 

  17. Schnabel, J.A., Heinrich, M.P., Papież, B.W., Brady, S.J.M.: Advances and challenges in deformable image registration: From image fusion to complex motion modelling. Med. Image Anal. 33, 145–148 (2016). https://doi.org/10.1016/j.media.2016.06.031

    Article  Google Scholar 

  18. Song, X., Wang, H., Venegas-Andraca, S.E., Abd El-Latif, A.A.: Quantum video encryption based on qubit-planes controlled-XOR operations and improved logistic map. Phys. A Stat. Mech. Appl. 537, 122660 (2020). https://doi.org/10.1016/j.physa.2019.122660

    Article  MathSciNet  MATH  Google Scholar 

  19. Guryanov, F., Krylov, A.: Fast medical image registration using bidirectional empirical mode decomposition. Signal Process. Image Commun. 59, 12–17 (2017). https://doi.org/10.1016/j.image.2017.04.003

    Article  Google Scholar 

  20. Yan, F., Venegas-Andraca, S.E., Hirota, K.: Toward implementing efficient image processing algorithms on quantum computers. Soft Comput. (2022). https://doi.org/10.1007/s00500-021-06669-2

    Article  Google Scholar 

  21. Huizinga, W., Poot, D.H.J., Guyader, J.-M., Klaassen, R., Coolen, B.F., van Kranenburg, M., van Geuns, R.J.M., Uitterdijk, A., Polfliet, M., Vandemeulebroucke, J., Leemans, A., Niessen, W.J., Klein, S.: PCA-based groupwise image registration for quantitative MRI. Med. Image Anal. 29, 65–78 (2016). https://doi.org/10.1016/j.media.2015.12.004

    Article  Google Scholar 

  22. Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: Fast predictive image registration—a deep learning approach. NeuroImage 158, 378–396 (2017). https://doi.org/10.1016/j.neuroimage.2017.07.008

    Article  Google Scholar 

  23. Yu, D., Yang, F., Yang, C., Leng, C., Cao, J., Wang, Y., Tian, J.: Fast rotation-free feature-based image registration using improved n-SIFT and GMM-based parallel optimization. IEEE Trans. Biomed. Eng. 63(8), 1653–1664 (2016). https://doi.org/10.1109/tbme.2015.2465855

    Article  Google Scholar 

  24. Jian, M., Liu, X., Luo, H., Lu, X., Yu, H., Dong, J.: Underwater image processing and analysis: A review. Signal Process. Image Commun. 91, 116088 (2021). https://doi.org/10.1016/j.image.2020.116088

    Article  Google Scholar 

  25. Landry, G., Nijhuis, R., Dedes, G., Handrack, J., Thieke, C., Janssens, G., de Xivry, J.O., Reiner, M., Kamp, F., Wilkens, J.J., Paganelli, C., Riboldi, M., Baroni, G., Ganswindt, U., Belka, C., Parodi, K.: Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation. Med. Phys. 42(3), 1354–1366 (2015). https://doi.org/10.1118/1.4908223

    Article  Google Scholar 

  26. Gupta, S., Gupta, P., Verma, V.S.: Study on anatomical and functional medical image registration methods. Neurocomputing 452, 534–548 (2021). https://doi.org/10.1016/j.neucom.2020.08.085

    Article  Google Scholar 

  27. Chen, Y., He, F., Zeng, X., Li, H., Liang, Y.: The explosion operation of fireworks algorithm boosts the coral reef optimization for multimodal medical image registration. Eng. Appl. Artif. Intell. 102, 104252 (2021). https://doi.org/10.1016/j.engappai.2021.104252

    Article  Google Scholar 

  28. Sengupta, D., Gupta, P., Biswas, A.: A survey on mutual information based medical image registration algorithms. Neurocomputing 486, 174–188 (2022). https://doi.org/10.1016/j.neucom.2021.11.023

    Article  Google Scholar 

  29. Azam, M.A., Khan, K.B., Ahmad, M., Mazzara, M.: Multimodal medical image registration and fusion for quality enhancement. Comput. Mater. Contin. 68(1), 821–840 (2021). https://doi.org/10.32604/cmc.2021.016131

    Article  Google Scholar 

  30. Bermejo, E., Chica, M., Damas, S., Salcedo-Sanz, S., Cordón, O.: Coral reef optimization with substrate layers for medical image registration. Swarm Evol. Comput. 42, 138–159 (2018). https://doi.org/10.1016/j.swevo.2018.03.003

    Article  Google Scholar 

  31. Bierbrier, J., Gueziri, H.-E., Collins, D.L.: Estimating medical image registration error and confidence: A taxonomy and scoping review. Med. Image Anal. 81, 102531 (2022). https://doi.org/10.1016/j.media.2022.102531

    Article  Google Scholar 

  32. Zachiu, C., de Senneville, B.D., Moonen, C.T.W., Raaymakers, B.W., Ries, M.: Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration. Phys. Med. Biol. 63(15), 155016 (2018). https://doi.org/10.1088/1361-6560/aad109

    Article  Google Scholar 

  33. Tang, K., Li, Z., Tian, L., Wang, L., Zhu, Y.: ADMIR–affine and deformable medical image registration for drug-addicted brain images. IEEE Access 8, 70960–70968 (2020). https://doi.org/10.1109/access.2020.2986829

    Article  Google Scholar 

  34. Alam, F., Rahman, S.U., Ullah, S., Gulati, K.: Medical image registration in image guided surgery: Issues, challenges and research opportunities. Biocybern. Biomed. Eng. 38(1), 71–89 (2018). https://doi.org/10.1016/j.bbe.2017.10.001

    Article  Google Scholar 

  35. Blendowski, M., Hansen, L., Heinrich, M.P.: Weakly-supervised learning of multi-modal features for regularised iterative descent in 3d image registration. Med. Image Anal. 67, 101822 (2021). https://doi.org/10.1016/j.media.2020.101822

    Article  Google Scholar 

  36. Saygili, G.: Predicting medical image registration error with block-matching using three orthogonal planes approach. Signal Image Video Process. 14(6), 1099–1106 (2020). https://doi.org/10.1007/s11760-020-01650-2

    Article  Google Scholar 

  37. Chen, M., Carass, A., Jog, A., Lee, J., Roy, S., Prince, J.L.: Cross contrast multi-channel image registration using image synthesis for MR brain images. Med. Image Anal. 36, 2–14 (2017). https://doi.org/10.1016/j.media.2016.10.005

    Article  Google Scholar 

  38. Heinrich, M.P., Simpson, I.J.A., Papież, B.W., Brady, S.M., Schnabel, J.A.: Deformable image registration by combining uncertainty estimates from supervoxel belief propagation. Med. Image Anal. 27, 57–71 (2016). https://doi.org/10.1016/j.media.2015.09.005

    Article  Google Scholar 

  39. Li, L., Luo, Z., He, F., Sun, K., Yan, X.: An improved partial similitude method for dynamic characteristic of rotor systems based on Levenberg–Marquardt method. Mech. Syst. Signal Process. 165, 108405 (2022). https://doi.org/10.1016/j.ymssp.2021.108405

    Article  Google Scholar 

  40. Klein, S., Pluim, J.P.W., Staring, M., Viergever, M.A.: Adaptive stochastic gradient descent optimisation for image registration. Int. J. Comput. Vis. 81(3), 227–239 (2008). https://doi.org/10.1007/s11263-008-0168-y

    Article  MATH  Google Scholar 

  41. Blais, A., Girvin, S.M., Oliver, W.D.: Quantum information processing and quantum optics with circuit quantum electrodynamics. Nat. Phys. 16(3), 247–256 (2020). https://doi.org/10.1038/s41567-020-0806-z

    Article  Google Scholar 

  42. Chen, K., Yan, F., Hirota, K., Zhao, J.: Quantum implementation of Powell’s conjugate direction method. J. Adv. Comput. Intell. Intell. Inf. 23(4), 726–734 (2019). https://doi.org/10.20965/jaciii.2019.p0726

    Article  Google Scholar 

  43. Chang, Y.-C.: N-dimension golden section search: Its variants and limitations. In: 2009 2nd International Conference on Biomedical Engineering and Informatics, pp. 1–6 (2009). https://doi.org/10.1109/BMEI.2009.5304779

  44. Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013). https://doi.org/10.1007/s11128-013-0567-z

    Article  MathSciNet  MATH  ADS  Google Scholar 

  45. Schmidt-Kaler, F., Häffner, H., Riebe, M., Gulde, S., Lancaster, G.P.T., Deuschle, T., Becher, C., Roos, C.F., Eschner, J., Blatt, R.: Realization of the Cirac–Zoller controlled-NOT quantum gate. Nature 422(6930), 408–411 (2003). https://doi.org/10.1038/nature01494

    Article  ADS  Google Scholar 

  46. Shepherd, D.J.: On the role of Hadamard gates in quantum circuits. Quantum Inf. Process. 5(3), 161–177 (2006). https://doi.org/10.1007/s11128-006-0023-4

    Article  MathSciNet  MATH  Google Scholar 

  47. Wang, J., Jiang, N., Wang, L.: Quantum image translation. Quantum Inf. Process. 14(5), 1589–1604 (2014). https://doi.org/10.1007/s11128-014-0843-6

    Article  MathSciNet  MATH  ADS  Google Scholar 

  48. Yan, F., Chen, K., Venegas-Andraca, S.E., Zhao, J.: Quantum image rotation by an arbitrary angle. Quantum Inf. Process. (2017). https://doi.org/10.1007/s11128-017-1733-5

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work is supported by the Natural Science Foundation of Jilin Province, China (Grant No. 20210101474JC).

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  1. School of Computer Science and Technology, Changchun University of Science and Technology, No. 7089, Weixing Road, Changchun, 130022, China

    Kehan Chen, Zhe Ren, Fei Yan & Jianping Zhao

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  1. Kehan Chen
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Correspondence to Jianping Zhao.

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Chen, K., Ren, Z., Yan, F. et al. Quantum implementation of image registration. Quantum Inf Process 22, 97 (2023). https://doi.org/10.1007/s11128-023-03834-4

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