An Image Registration Approach Based on 3D Geometric Projection Similarity of the Human Head
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Abstract
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.
Keywords
Geometry projection similarity Geometry shape information Image registration Iterative closest point Maximal mutual informationNotes
Acknowledgements
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.
References
- 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.Doi, K. (2014). Current status and future potential of computer-aided diagnosis in medical imaging. The British Journal of Radiology. https://doi.org/10.1259/bjr/82933343.Google Scholar
- 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.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.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.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.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.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.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.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.Sotiras, A., Davatzikos, C., & Paragios, N. (2013). Deformable medical image registration: A survey. IEEE Transactions on Medical Imaging, 32, 1153–1190.CrossRefGoogle Scholar
- 12.Zitova, B., & Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21, 977–1000.CrossRefGoogle Scholar
- 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.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.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.Besl, P. J. & McKay, N. D. (1992). Method for registration of 3-D shapes. In Robotics-DL tentative (pp. 586–606).Google Scholar
- 17.Segal, A., Haehnel, D., & Thrun, S. (2009). Generalized-ICP. In Robotics: Science and systems.Google Scholar
- 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.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.Crum, W. R., Hartkens, T., & Hill, D. (2014). Non-rigid image registration: Theory and practice. The British Journal of Radiology. https://doi.org/10.1259/bjr/25329214.Google Scholar
- 21.Hartigan, J. A., & Wong, M. A. (1979). A K-means clustering algorithm. Applied Statistics, 28, 100–108.CrossRefzbMATHGoogle Scholar
- 22.Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning (pp. 760–766). Heidelberg: Springer.Google Scholar
- 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.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.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.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.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.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.Periaswamy, S., & Farid, H. (2006). Medical image registration with partial data. Medical Image Analysis, 10, 452–464.CrossRefGoogle Scholar
- 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.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.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.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.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.Powell, M. J. D. (1977). Restart procedures for the conjugate gradient method. Mathematical Programming, 12, 241–254.MathSciNetCrossRefzbMATHGoogle Scholar
- 36.Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1, 28–39.CrossRefGoogle Scholar
- 37.Hwang, C.-R. (1988). Simulated annealing: Theory and applications. Acta Applicandae Mathematicae, 12, 108–111.Google Scholar
- 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.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.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