Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Feature-based Non-rigid Registration between Pre- and Post-Contrast Lung CT Images

조영 전후의 폐 CT 영상 정합을 위한 특징 기반의 비강체 정합 기법

  • Lee, Hyun-Joon (School of Electrical Engineering, Automation and Systems Research Institute(ASRI), BK21 Research Division for Information Technology, Seoul National University) ;
  • Hong, Young-Taek (Department of Digital and Information Engineering, Hankuk University of Foreign Studies) ;
  • Shim, Hack-Joon (Cardiovascular Research Institute, Yonsei University, College of Medicine) ;
  • Kwon, Dong-Jin (School of Electrical Engineering, Automation and Systems Research Institute(ASRI), BK21 Research Division for Information Technology, Seoul National University) ;
  • Yun, Il-Dong (Department of Digital and Information Engineering, Hankuk University of Foreign Studies) ;
  • Lee, Sang-Uk (School of Electrical Engineering, Automation and Systems Research Institute(ASRI), BK21 Research Division for Information Technology, Seoul National University) ;
  • Kim, Nam-Kug (Department of Radiology, University of Ulsan, College of Medicine, Asan Medical Center) ;
  • Seo, Joon-Beom (Department of Radiology, University of Ulsan, College of Medicine, Asan Medical Center)
  • 이현준 (서울대학교 전기공학부, 자동화시스템공동연구소, BK21정보기술사업단) ;
  • 홍영택 (한국외국어대학교 디지털정보공학과) ;
  • 심학준 (연세대학교 의과대학, 심혈관연구소) ;
  • 권동진 (서울대학교 전기공학부, 자동화시스템공동연구소, BK21정보기술사업단) ;
  • 윤일동 (한국외국어대학교 디지털정보공학과) ;
  • 이상욱 (서울대학교 전기공학부, 자동화시스템공동연구소, BK21정보기술사업단) ;
  • 김남국 (울산대학교 의과대학 서울아산병원, 영상의학과) ;
  • 서준범 (울산대학교 의과대학 서울아산병원, 영상의학과)
  • Received : 2011.02.28
  • Accepted : 2011.08.31
  • Published : 2011.09.30
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Abstract

In this paper, a feature-based registration technique is proposed for pre-contrast and post-contrast lung CT images. It utilizes three dimensional(3-D) features with their descriptors and estimates feature correspondences by nearest neighborhood matching in the feature space. We design a transformation model between the input image pairs using a free form deformation(FFD) which is based on B-splines. Registration is achieved by minimizing an energy function incorporating the smoothness of FFD and the correspondence information through a non-linear gradient conjugate method. To deal with outliers in feature matching, our energy model integrates a robust estimator which discards outliers effectively by iteratively reducing a radius of confidence in the minimization process. Performance evaluation was carried out in terms of accuracy and efficiency using seven pairs of lung CT images of clinical practice. For a quantitative assessment, a radiologist specialized in thorax manually placed landmarks on each CT image pair. In comparative evaluation to a conventional feature-based registration method, our algorithm showed improved performances in both accuracy and efficiency.

Keywords

References

  1. B. Zitova and J. Flusser, "Image registration methods : a survey," Image and Vision Computing, vol. 21, no. 11, pp. 977-1000, 2003. https://doi.org/10.1016/S0262-8856(03)00137-9
  2. B. Li, G.E. Christensen, G. McLennan, E.A. Hoffman, and J.M. Reinhardt, "Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3-D consistent image registration," Medical Physics, vol. 35, no. 12, pp. 5575-5583, 2008. https://doi.org/10.1118/1.3005633
  3. K. Ding, J.E. Bayouth, J.M. Buatti, G.E. Christensen, and J.M. Reinhardt, "4DCT based measurement of changes in pulmonary function following a course of radiation therapy," Medical Physics, vol. 37, no. 3, pp. 1261-1272, 2010. https://doi.org/10.1118/1.3312210
  4. M. Urschler, J. Bauer, H. Ditt, and H. Bischof, "Automatic point landmark matching for regularizing nonlinear intensity registration: Application to thoracic CT images," In Proc. of MICCAI, LNCS. 4191, pp. 710-717, 2006.
  5. M. Betke, H. Hong, D. Thomas, C. Prince, and J.P. Ko, "Landmark detection in the chest and registration of lung surfaces with an application to nodule registration," Medical Image Analysis, vol. 7, no. 3, pp. 265-281, 2003. https://doi.org/10.1016/S1361-8415(03)00007-0
  6. D.G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  7. M. Urschler, J. Bauer, H. Ditt, and H. Bischof, "SIFT and shape context for feature-based nonlinear registration of thoracic CT images," In Computer Vision Approaches to Medical Image Analysis, pp. 73-84, 2006.
  8. S. Lee, G. Wolberg, and S.Y. Shin, "Scattered data interpolation with multi-level B-splines," IEEE Trans. on Visualization and Computer Graphics, vol. 3, no. 3, pp. 228-244, 1997. https://doi.org/10.1109/2945.620490
  9. D. Rueckert, L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach, and D.J. Hawkes, "Nonrigid registration using free-form deformations: application to breast MR images," IEEE Trans. on Medical Imaging, vol. 18, no. 8, pp .712-721, 1999. https://doi.org/10.1109/42.796284
  10. C.V. Stewart, Y. Lee, and C. Tsai, "An uncertainty-driven hybrid of intensity-based and feature-based registration with application to retinal and lung CT images," In Proc. of MICCAI, pp. 870-877, 2004.
  11. Y. Yin, E.A. Hoffman, K. Ding, J.M. Reinhardt, and C. Lin, "A cubic B-spline-based hybrid registration of lung CT images for a dynamic airway geometric model with large deformation," Medicine and Biology, vol. 56, no. 1, pp. 203-218, 2011. https://doi.org/10.1088/0031-9155/56/1/013
  12. W. Frstner, "A feature based correspondence algorithm for image matching," Int. Arch. Photogram. Remote Sensing, vol. 26, pp. 150-166, 1986.
  13. J. Pilet, V. Lepetit, and P. Fua, "Fast non-rigid surface detection, registration and realistic augmentation," Int. Journal of Computer Vision, vol. 76, no. 2, pp. 109-122, 2008. https://doi.org/10.1007/s11263-006-0017-9
  14. J.R. Shewchuk, "An introduction to the conjugate gradient method without the agonizing pain," 1994.
  15. S. Allaire, J. Kim, S. Breen, D. Haffray, and V. Pekar, "Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis," In Proc. of MMBIA., 2008.
  16. D. Kwon, I. Yun, K. Lee, and S. Lee, "Efficient feature-based nonrigid registration of multiphase liver CT volumes," In Proc. of BMVC, 2008.
  17. X. Han, "Feature-constrained nonlinear registration of lung CT images," In MICCAI 2010 Grand Challenges in Medical Image Analysis: Evaluation of Methods for Pulmonary Image Registration(EMPIRE10), 2010.