A feature-based solution for 3D registration of CT and MRI images of human knee

Abstract

This paper presents a feature-based solution for 3D registration of CT and MRI images of a human knee. It facilitates constructing high-quality models with clear outlining of bone tissues and detailed illustration of soft tissues. The model will be used for analysing the effect of posterior cruciate ligament and anterior cruciate ligament deficiency. The solution consists of preprocessing, feature extraction, transformation parameter estimation and resampling, and blending. In preprocessing, we propose partial preserving and iterative neighbour comparing filtering to help segment bone tissues from MRI images without having to construct a statistical model. Through analysing the characteristics of knee images, tibia and femur are selected as the features and the algorithm for effectively extracting them is described. To estimate transformation parameters, we propose a method based on the statistical information of projected feature images, including translating according to the project feature image centroids and calculating the rotation angle by searching and mapping boundary points. We transform the MRI image and then blend it with the CT image by taking the maximum intensity of every two corresponding voxels from the two images. At the end of the paper, the registration result is evaluated by computing the Pearson product-moment correlation coefficient of the binarised features and the accuracy is confirmed.

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References

  1. 1.

    Fripp, J., Warfield, S.K., Crozier, S., Ourselin, S.: In: Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, Vol. 1 (IEEE, 2006), pp. 167–170

  2. 2.

    Goshtasby, A.A.: Image Registration: Principles Tools and Methods. Springer, New York (2012)

    Google Scholar 

  3. 3.

    Alam, M.M., Howlader, T., Rahman, S.M.: Entropy-based image registration method using the curvelet transform. Signal Image Video Process. 8(3), 491–505 (2014)

  4. 4.

    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187 (1997)

    Article  Google Scholar 

  5. 5.

    Kim, J., Fessler, J.A.: Intensity-based image registration using robust correlation coefficients. IEEE Trans. Med. Imaging 23(11), 1430 (2004)

    Article  Google Scholar 

  6. 6.

    Bouchiha, R., Besbes, K.: Comparison of local descriptors for automatic remote sensing image registration. Signal Image Video Process. 1–7 (2013)

  7. 7.

    Can, A., Stewart, C.V., Roysam, B., Tanenbaum, H.L.: A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 347 (2002)

    Article  Google Scholar 

  8. 8.

    Dai, X., Khorram, S.: A feature-based image registration algorithm using improved chain-code representation combined with invariant moments. IEEE Trans. Geosci. Remote Sens. 37(5), 2351 (1999)

    Article  Google Scholar 

  9. 9.

    Boda, S.: In: Feature-Based Image Registration. Ph.D. thesis (2009)

  10. 10.

    Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977 (2003)

    Article  Google Scholar 

  11. 11.

    Tomaževič, D., Likar, B., Pernuš, F.: Multi-feature mutual information image registration. Image Anal. Stereol. 31(1), 43 (2012)

    MathSciNet  Article  Google Scholar 

  12. 12.

    Ji, Z., Wei, H.: The registration of knee joint images with preprocessing. Int. J. Image Graphics Signal Proc. (IJIGSP) 3(4), 10 (2011)

  13. 13.

    Powell, M. J.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155–162 (1964)

  14. 14.

    Pan, X., Zhao, K., Liu, J., Kang, Y.: In: Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on, Vol. 1 (IEEE, 2010), pp. 18–22

  15. 15.

    Kapur, T., Beardsley, P., Gibson, S., Grimson, W., Wells, W.: In: Proceedings of IEEE Intl Workshop on Model-Based 3D Image Analysis (Citeseer, 1998), pp. 97–106

  16. 16.

    Poynton, C.: Digital Video and HD: Algorithms and Interfaces. Morgan Kaufmann, Burlington, Massachusetts (2012)

  17. 17.

    Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23 (1975)

  18. 18.

    Roberts, L.G.: In: Machine Perception of Three-Dimensional Solids. Technical Report, DTIC Document (1963)

  19. 19.

    Shah, P., Srikanth, T., Merchant, S.N.: In: U.B. Desai, Signal, Image and Video Processing pp. 1–16 (2013)

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Correspondence to Zhenyan Ji.

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This paper is sponsored by Fundamental Research Funds for Chinese Central Universities (No. R12JB00020) and National Natural Science Foundation of China (No. 50975013). Jingjie Zheng and Zhenyan Ji contributed equally to this work.

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Zheng, J., Ji, Z., Yu, K. et al. A feature-based solution for 3D registration of CT and MRI images of human knee. SIViP 9, 1815–1824 (2015). https://doi.org/10.1007/s11760-014-0660-5

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Keywords

  • Feature-based image registration
  • Magnetic resonance imaging
  • Computed tomography
  • Human knee