Journal of Medical and Biological Engineering

, Volume 39, Issue 1, pp 126–138 | Cite as

An Image Registration Approach Based on 3D Geometric Projection Similarity of the Human Head

  • Jun Liu
  • Yun Peng
  • Hao Chen
  • Thomas Potter
  • Yingchun ZhangEmail author
Original Article


This study aims to develop a novel brain image registration algorithm, the geometry projection similarity (GPS) algorithm, which utilizes the anatomical feature of human head and simplifies the objective function for optimization and search for registration parameters in a step-by-step fashion, largely reducing the solution space and improving the efficiency. The algorithm was tested and compared with existing algorithms in simulation environment and tested on computed tomography/magnetic resonance imaging (CT/MRI) clinical datasets pre- and post-craniotomy surgery. Compared with the maximal mutual information algorithm, the GPS algorithm showed significantly smaller errors (1.2 ± 1.1 vs. 4.5 ± 3.5, 1.0 ± 1.0 vs. 3.5 ± 2.5, 0.8 ± 1.1 vs. 2.8 ± 2.4 pixels, at cluster size of 100, 150 and 200, respectively) and shorter computational durations. Compared with the iterative closest point algorithm, the GPS algorithm showed a greater robustness to the perturbed initial mismatch between registering images (registration failure rate 0 vs. 29 out of 200 trials), while keeping a satisfactorily low registration error (0.71 ± 0.83 pixels). Overall results demonstrated that registration accuracy, efficiency, and robustness of this new algorithm surpass those of existing methods. Pilot test on CT/MRI clinical datasets also showed a satisfactory outcome, demonstrating great potentials for future applications of registration of brain images across modalities.


Geometry projection similarity Geometry shape information Image registration Iterative closest point Maximal mutual information 



This research was partly supported by National Nature Science Foundation of China (Grant No. 61462063), Natural Science Foundation of Jiangxi Province (Grant No. 20151BAB205050), Foundation of Jiangxi Educational Committee (Grant No. GJJ14503), Guangdong Provincial Work Injury Rehabilitation Center and the University of Houston.


  1. 1.
    Hollingworth, W., Todd, C. J., Bell, M. I., Arafat, Q., Girling, S., Karia, K. R., et al. (2000). The diagnostic and therapeutic impact of MRI: An observational multi-centre study. Clinical Radiology, 55, 825–831.CrossRefGoogle Scholar
  2. 2.
    Doi, K. (2014). Current status and future potential of computer-aided diagnosis in medical imaging. The British Journal of Radiology. Scholar
  3. 3.
    Markelj, P., Tomaževič, D., Likar, B., & Pernuš, F. (2012). A review of 3D/2D registration methods for image-guided interventions. Medical Image Analysis, 16, 642–661.CrossRefGoogle Scholar
  4. 4.
    Cavalcante, J. L., Lalude, O. O., Schoenhagen, P., & Lerakis, S. (2016). Cardiovascular magnetic resonance imaging for structural and valvular heart disease interventions. JACC: Cardiovascular Interventions, 9, 399–425.Google Scholar
  5. 5.
    Peng, Y., Khavari, R., Nakib, N. A., Boone, T. B., & Zhang, Y. (2016). Assessment of urethral support using MRI-derived computational modeling of the female pelvis. International Urogynecology Journal, 27, 205–212.CrossRefGoogle Scholar
  6. 6.
    Liu, Y., & Zhang, Y. (2014). A feasibility study of magnetic resonance electrical impedance tomography for prostate cancer detection. Physiological Measurement, 35, 567.CrossRefGoogle Scholar
  7. 7.
    El-Gamal, F. E.-Z. A., Elmogy, M., & Atwan, A. (2016). Current trends in medical image registration and fusion. Egyptian Informatics Journal, 17, 99–124.CrossRefGoogle Scholar
  8. 8.
    Marks, W. A., Honeycutt, J., Acosta, F., Reed, M. A., Bailey, L., Pomykal, A., et al. (2014). Registration of CT to pre-treatment MRI for planning of MR-HIFU ablation treatment of painful bone metastases. Physics in Medicine and Biology, 59, 4167–4179.CrossRefGoogle Scholar
  9. 9.
    Kessler, M. L. (2006). Image registration and data fusion in radiation therapy. British Journal of Radiology, 79(Spec No 1), 83–112.MathSciNetGoogle Scholar
  10. 10.
    Crum, W. R., Hartkens, T., & Hill, D. L. (2004). Non-rigid image registration: Theory and practice. British Journal of Radiology, 77(Spec No 2), S140.CrossRefGoogle Scholar
  11. 11.
    Sotiras, A., Davatzikos, C., & Paragios, N. (2013). Deformable medical image registration: A survey. IEEE Transactions on Medical Imaging, 32, 1153–1190.CrossRefGoogle Scholar
  12. 12.
    Zitova, B., & Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21, 977–1000.CrossRefGoogle Scholar
  13. 13.
    Tennakoon, R. B., Bab-Hadiashar, A., Cao, Z., & De, B. M. (2014). Nonrigid registration of volumetric images using ranked order statistics. IEEE Transactions on Medical Imaging, 33, 422–432.CrossRefGoogle Scholar
  14. 14.
    Damas, S., Cordón, O., & Santamaría, J. (2011). Medical image registration using evolutionary computation: An experimental survey. IEEE Computational Intelligence Magazine, 6, 26–42.CrossRefGoogle Scholar
  15. 15.
    Klein, S., Staring, M., & Pluim, J. P. (2007). Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE Transactions on Image Processing, 16, 2879–2890.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Besl, P. J. & McKay, N. D. (1992). Method for registration of 3-D shapes. In Robotics-DL tentative (pp. 586–606).Google Scholar
  17. 17.
    Segal, A., Haehnel, D., & Thrun, S. (2009). Generalized-ICP. In Robotics: Science and systems.Google Scholar
  18. 18.
    Xie, W., Nolte, L.-P., & Zheng, G. (2011). ECM versus ICP for point registration. In Engineering in Medicine and Biology Society, EMBC, 2011 annual international conference of the IEEE (pp. 2131–2135).Google Scholar
  19. 19.
    Milella, A. & Siegwart, R. (2006). Stereo-based ego-motion estimation using pixel tracking and iterative closest point. In IEEE international conference on computer vision systems, 2006, ICVS’06 (pp. 21–21).Google Scholar
  20. 20.
    Crum, W. R., Hartkens, T., & Hill, D. (2014). Non-rigid image registration: Theory and practice. The British Journal of Radiology. Scholar
  21. 21.
    Hartigan, J. A., & Wong, M. A. (1979). A K-means clustering algorithm. Applied Statistics, 28, 100–108.CrossRefzbMATHGoogle Scholar
  22. 22.
    Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning (pp. 760–766). Heidelberg: Springer.Google Scholar
  23. 23.
    Chipperfield, A. & Fleming, P. (1995). The MATLAB genetic algorithm toolbox. In IEE colloquium on applied control techniques using MATLAB (pp. 10/1–10/4).Google Scholar
  24. 24.
    Stoll, K. E., Miles, J. D., White, J. K., Punt, S. E., Conrad, E. U., III, & Ching, R. P. (2015). Assessment of registration accuracy during computer-aided oncologic limb-salvage surgery. International Journal of Computer Assisted Radiology and Surgery, 10, 1469–1475.CrossRefGoogle Scholar
  25. 25.
    Hill, D. L., Batchelor, P. G., Holden, M., & Hawkes, D. J. (2001). Medical image registration. Physics in Medicine and Biology, 46, 1–45.CrossRefGoogle Scholar
  26. 26.
    Karnik, V., Fenster, A., Bax, J., Cool, D., Gardi, L., Gyacskov, I., et al. (2010). Assessment of image registration accuracy in three-dimensional transrectal ultrasound guided prostate biopsy. Medical Physics, 37, 802–813.CrossRefGoogle Scholar
  27. 27.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997). Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16, 187–198.CrossRefGoogle Scholar
  28. 28.
    Pluim, J. P., Maintz, J. A., & Viergever, M. A. (2003). Mutual-information-based registration of medical images: A survey. IEEE Transactions on Medical Imaging, 22, 986–1004.CrossRefGoogle Scholar
  29. 29.
    Periaswamy, S., & Farid, H. (2006). Medical image registration with partial data. Medical Image Analysis, 10, 452–464.CrossRefGoogle Scholar
  30. 30.
    Li, J., Wang, K., Zhu, S., & He, B. (2007). Effects of holes on EEG forward solutions using a realistic geometry head model. Journal of Neural Engineering, 4, 197.CrossRefGoogle Scholar
  31. 31.
    Yao, J., & Goh, K. L. (2006). A refined algorithm for multisensor image registration based on pixel migration. IEEE Transactions on Image Processing, 15, 1839–1847.CrossRefGoogle Scholar
  32. 32.
    Pomerleau, F., Colas, F., & Siegwart, R. (2015). A review of point cloud registration algorithms for mobile robotics. Foundations and Trends in Robotics (FnTROB), 4, 1–104.CrossRefGoogle Scholar
  33. 33.
    Rohlfing, T. (2012). Image similarity and tissue overlaps as surrogates for image registration accuracy: Widely used but unreliable. IEEE Transactions on Medical Imaging, 31, 153–163.CrossRefGoogle Scholar
  34. 34.
    Hassan, R., Cohanim, B., De Weck, O., & Venter, G. (2005). A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the 1st AIAA multidisciplinary design optimization specialist conference (p. e21).Google Scholar
  35. 35.
    Powell, M. J. D. (1977). Restart procedures for the conjugate gradient method. Mathematical Programming, 12, 241–254.MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1, 28–39.CrossRefGoogle Scholar
  37. 37.
    Hwang, C.-R. (1988). Simulated annealing: Theory and applications. Acta Applicandae Mathematicae, 12, 108–111.Google Scholar
  38. 38.
    Justice, R. K., Stokely, E. M., Strobel, J. S., Ideker, R. E., & Smith, W. M. (1997). Medical image segmentation using 3-D seeded region growing. In Proceedings of SPIE symposium on medical imaging (pp. 900–910).Google Scholar
  39. 39.
    Jiang, C. F., Huang, C. H., & Yang, S. T. (2010). Using maximal cross-section detection for the registration of 3D image data of the head. Journal of Medical and Biological Engineering, 31, 217–225.CrossRefGoogle Scholar
  40. 40.
    Arata, L. K., Dhawan, A. P., Broderick, J. P., & Gaskil-Shipley, M. F. (1995). Three-dimensional anatomical model-based segmentation of MR brain images through principal axes registration. IEEE Transactions on Biomedical Engineering, 42, 1069–1078.CrossRefGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2018

Authors and Affiliations

  1. 1.Department of Information EngineeringNanchang Hangkong UniversityNanchangChina
  2. 2.Department of Biomedical EngineeringUniversity of HoustonHoustonUSA
  3. 3.Guangdong Provincial Work Injury Rehabilitation CenterGuangzhouChina

Personalised recommendations