Ray-Tracing Based Registration for HRCT Images of the Lungs

  • Sata Busayarat
  • Tatjana Zrimec
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Image registration is a fundamental problem in medical imaging. It is especially challenging in lung images compared, for example, with the brain. The challenges include large anatomical variations of human lung and a lack of fixed landmarks inside the lung. This paper presents a new method for lung HRCT image registration. It employs a landmark-based global transformation and a novel ray-tracing-based lung surface registration. The proposed surface registration method has two desirable properties: 1) it is fully reversible, and 2) it ensures that the registered lung will be inside the target lung. We evaluated the registration performance by applying it to lung regions mapping. Tested on 46 scans, the registered regions were 89% accurate compared with the ground-truth.


Image Registration Registration Method Lung Region Lung Model Lung Surface 
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  1. 1.
    Sonka, M., Fitzpatrick, J.: Handbook of Medical Imaging, vol. 2. SPIE Press (2000)Google Scholar
  2. 2.
    Ferrant, M., Nabavi, A., Macq, B., Jolesz, F., Kikinis, R., Warfield, S.: Registration of 3-D interoperative MR images of the brain using a finite-element biomechanical model. IEEE Trans. Med. Imag. 20, 129–140 (2001)CrossRefGoogle Scholar
  3. 3.
    Kapouleas, I., Alavi, A., Alves, W., Gur, R., Weiss, D.: Registration of three-dimensional MR and PET images of the human brain without markers. Radiology 181, 731–739 (1991)Google Scholar
  4. 4.
    Zhang, L., Reinhardt, J.: 3D pulmonary CT image registration with a standard lung atlas. In: SPIE, vol. 3978, pp. 67–77 (2000)Google Scholar
  5. 5.
    Betke, M., Hong, H., Thomas, D., Prince, C., Ko, J.: Landmark detection in the chest and registration of lung surfaces with an application to nodule registration. Medical Image Analysis 7, 265–281 (2003)CrossRefGoogle Scholar
  6. 6.
    Betke, M., Hong, H., Ko, J.: Automatic 3D Registration of lung surfaces in computed tomography scans. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 725–733. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Papasoulis, J.: LMIK – Anatomy and lung measurements using active contour snakes. Master’s thesis, UNSW, Sydney, Australia (2003)Google Scholar
  8. 8.
    Zrimec, T., Busayarat, S.: A 3d model of the human lung with lung regions characterization. In: ICIP, pp. 1149–1152 (2004)Google Scholar
  9. 9.
    Glassner, A.: An Introduction to Ray Tracing. Academic Press, London (1989)MATHGoogle Scholar
  10. 10.
    Foley, J.: Introduction to Computer Graphics. Addison-Wesley, Reading (1994)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sata Busayarat
    • 1
  • Tatjana Zrimec
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.Centre for Health InformaticsUniversity of New South WalesSydneyAustralia

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