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Multimodal image registration system for image-guided orthopaedic surgery

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Abstract

We present a novel multimodality image registration system for spinal surgery. The system comprises a surface-based algorithm that performs computed tomography/magnetic resonance (CT/MR) rigid registration and MR image segmentation in an iterative manner. The segmentation/registration process progressively refines the result of MR image segmentation and CT/MR registration. For MR image segmentation, we propose a method based on the double-front level set that avoids boundary leakages, prevents interference from other objects in the image, and reduces computational time by constraining the search space. In order to reduce the registration error from the misclassification of the soft tissue surrounding the bone in MR images, we propose a weighted surface-based CT/MR registration scheme. The resultant weighted surface is registered to the segmented surface of the CT image. Contours are generated from the reconstructed CT surfaces for subsequent MR image segmentation. This process iterates till convergence. The registration method achieves accuracy comparable to conventional techniques while being significantly faster. Experimental results demonstrate the advantages of the proposed approach and its application to different anatomies.

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References

  1. Jan M.L., Chuang K.S., Chen G.W. et al.: A three-dimensional registration method for automated fusion of micro PET-CT-SPECT whole-body images. IEEE Trans. Med. Imaging 24, 886–893 (2005)

    Article  Google Scholar 

  2. Stout, D., Chow, P., Silverman, B., Leahy, R., Lewis, X., Gambhir, S., Chatziioannou, A.: Creating a whole body digital mouse atlas with PET, CT and cryosection images. In: Proceedings AMI Annual Conference, San Diego, CA, pp. 23–27 (2002)

  3. Hill, D.L.G., Batchelor, P.: Registration methodology: concepts and algorithms. In: Hajnal, J., Hawkes, D., Hill, D. (eds.) Medical Image Registration, Chap 3 (2000)

  4. Ding L., Goshtasby A., Satter M.: Volume image registration by template matching. Image Vis. Comput. 19, 821–832 (2001)

    Article  Google Scholar 

  5. Pluim J.P.W., Maintz J.B.A., Viergever M.A.: Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Med. Imaging 19, 809–814 (2000)

    Article  Google Scholar 

  6. Wang Y., Peterson B.S., Staib L.H.: Shape-based 3D surface correspondence using geodesics and local geometry. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2, 644–651 (2000)

    Article  Google Scholar 

  7. Besl P.J., McKay N.D.: A method for registration of 3D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)

    Article  Google Scholar 

  8. Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. In: International Conference on Computer Vision. IEEE Computer Society Press, Los Alamitos, CA, pp. 16–23 (1995)

  9. Chen H., Varshney P.K.: Mutual information based CT-MR brain image registration using generalized partial volume joint histogram estimation. IEEE Trans. Med. Imaging 22, 1111–1119 (2003)

    Article  Google Scholar 

  10. Leventon, M.E., Grimson, W.E.L.: Multimodal volume registration using joint intensity distribution. In: Proceeding of MICCAI ‘98, pp. 1057–1066. Berlin, Heidelberg (1998)

  11. Okker, B.H., Yan, C.H., Zhang, J., Ong, S.H., Teoh, S.H.: Accurate and fully automatic 3D registration of spinal images using normalized mutual information. In: IEEE International Workshop on BioMedical Circuits & Systems, Singapore, 1–3 December 2004

  12. Chui C.K., Li Z.R., Anderson J.H. et al.: Training and pretreatment planning of interventional neuroradiology procedures—initial clinical validation. Stud. Health Technol. Inform. 85, 96–102 (2002)

    Google Scholar 

  13. Ma B., Ellis R.E.: Robust registration for computer-integrated orthopedic surgery: laboratory validation and clinical experience. Med. Image Anal. 7, 237–250 (2003)

    Article  Google Scholar 

  14. Fitzgibbon A.W.: Robust registration of 2D and 3D point sets. Image. Vis. Comput. 21, 1145–1153 (2003)

    Article  Google Scholar 

  15. Fiebich M., Straus C.M., Sehgal V., Renger B.C., Doi K., Hoffmann K.R.: Automatic bone segmentation technique for CT angiographic studies. J. Comput. Assist. Tomogr. 23, 155–161 (1999)

    Article  Google Scholar 

  16. Pham D.L., Xu C., Prince J.L.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 315–337 (2000)

    Article  Google Scholar 

  17. Jasjit, S.S., Sameer, S.: Advanced algorithmic approaches to medical image segmentation, state-of-the-art applications in cardiology, neurology, mammography and pathology, vol. 238. Springer Verlag (2002)

  18. Pluempitiwiriyawej C., Moura J.M.F., Wu Y.J.L., Ho C.: STACS: new active contour scheme for cardiac MR image segmentation. IEEE Trans. Med. Imaging 24, 593–603 (2005)

    Article  Google Scholar 

  19. Van L.K., Maes F., Vandermeulen D., Suetens P.: A unifying framework for partial volume segmentation of brain MR images. IEEE Trans. Med. Imaging 22, 105–119 (2003)

    Article  Google Scholar 

  20. Algorri M.E., Mangas F.F.: Classification of anatomical structures in MR brain images using fuzzy parameters. IEEE Trans. Biomed. Eng. 51, 1599–1608 (2004)

    Article  Google Scholar 

  21. Selvathi, D., Arulmurgan, A., Thamarai, S.S., Alagappan, S.: MRI image segmentation using unsupervised clustering techniques. In: Six International Conference on Computational Intelligence and Multimedia Applications, pp. 105–110, 16–18 August 2005

  22. Shan S., Sandham W., Granat M., Sterr A.: MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans. Inf. Technol. Biomed. 9, 459–467 (2005)

    Article  Google Scholar 

  23. Lapeer R.J., Tan A.C., Aldridge R.: Active watersheds: combining 3D watershed segmentation and active contours to extract abdominal organs from MR images. In: Dohi, T., Kikinis, R. (eds) Proceedings of Medical Image Computing and Computer-Assisted Intervention-MICCAI 2002: 5th International Conference, Tokyo, Japan, 25–28 September. Lecture Notes in Computer Science, pp. 596–603. Springer, Heidelberg (2002)

    Google Scholar 

  24. Hsiao Y.T., Chuang C.L., Jiang J.A., Chien C.C.: A contour based image segmentation algorithm using morphological edge detection. IEEE Int. Conf. Syst. Man Cybern. 3, 2962–2967 (2005)

    Article  Google Scholar 

  25. Wu Z., Paulsen K.D., Sullivan J.M. Jr: Adaptive model initialization and deformation for automatic segmentation of T1-weighted brain MRI data. IEEE Trans. Biomed. Eng. 52, 1128–1131 (2005)

    Article  Google Scholar 

  26. Sethian J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. USA 93, 1591–1595 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  27. Munim, H.E.A.E., Farag, A.A.: A shape-based segmentation approach: an improved technique using level sets. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 930–935 (2005)

  28. Jing Y., Staib L.H., Duncan J.S.: Neighbor-constrained segmentation with level set based 3-D deformable models. IEEE Trans. Med. Imaging 23, 940–948 (2004)

    Article  Google Scholar 

  29. Zhang J., Ge Y., Ong S.H., Chui C.K., Teoh S.H., Yan C.H.: Rapid surface registration of 3D volumes using a neural network approach. Image Vis. Comput. 26, 201–210 (2008)

    Article  Google Scholar 

  30. Yan, C.H., Ong, S.H., Ge, Y., Zhang, J., Teoh, S.H., Okker, B.H.: A neural network approach for 3D surface modeling and registration. In: IEEE International Workshop on BioMedical Circuits & Systems, Singapore, 1–3 December 2004

  31. Malladi R., Sethian J.A., Vemuri B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17, 158–175 (1995)

    Article  Google Scholar 

  32. Perona P., Malik J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)

    Article  Google Scholar 

  33. Belahmidil A., Chambolle A.: Time-delay regularization of anisotropic diffusion and image processing. Math. Model. Numer. Anal. 39, 231–251 (2005)

    Article  Google Scholar 

  34. Sethian, J.A.: An analysis of flame propagation, Ph.D. thesis, Department of Mathematics. University of California, Berkeley, CA (1982)

  35. Rouy E., Tourin A.: A viscosity solutions approach to shape-from-shading. SIAM J. Numer. Anal. 29, 867–884 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  36. Yezzi A., Zollei L., Kapur T.: A variational framework for integrating segmentation and registration through active contours. Med. Image Anal. 7, 171–185 (2003)

    Article  Google Scholar 

  37. Maurer C.R. Jr, Maciunas R.J., Fitzpatrick J.M.: Registration of head CT images to physical space using a weighted combination of points and surfaces. IEEE Trans. Med. Imaging 17, 753–761 (1998)

    Article  Google Scholar 

  38. Slagmolen, P., Loeckx, D., Roels, S., Geets, X., Maes, F., Haustermans, K., Suetens, P.: Nonrigid registration of multitemporal CT and MR images for radiotherapy treatment planning. In: Biomedical Image Registration. Lecture Notes in Computer Science, pp. 297–305. Springer, Heidelberg (2006)

  39. Li, H., Elmoataz, A., Fadili, J., Ruan, S.: Dual front evolution model and its application in medical imaging. In: MICCAI 2004, pp. 103–110 (2004)

  40. Li H., Yezzi A.: Local or global minima: flexible dual-front active contours. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1–14 (2007)

    Article  Google Scholar 

  41. Wang S.T., Ma H.L., Liu C.L., Yu W.K., Chang M.C., Chen T.H.: Is fusion necessary for surgically treated burst fractures of the thoracolumbar and lumbar spine? a prospective, randomized study. Spine 31, 2646–2652 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore

    J. Zhang, C. H. Yan & S. H. Ong

  2. Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore

    C. K. Chui

  3. Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    J. Zhang & S. C. Wang

  4. Computer Vision & Image Understanding Department, Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis, Singapore, Singapore

    J. Zhang

  5. Division of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore

    S. H. Ong

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  1. J. Zhang
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  2. C. H. Yan
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  3. C. K. Chui
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  4. S. H. Ong
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  5. S. C. Wang
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Correspondence to S. H. Ong.

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Zhang, J., Yan, C.H., Chui, C.K. et al. Multimodal image registration system for image-guided orthopaedic surgery. Machine Vision and Applications 22, 851–863 (2011). https://doi.org/10.1007/s00138-010-0302-z

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  • DOI: https://doi.org/10.1007/s00138-010-0302-z

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