Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
권호 표지
DOI QR코드

DOI QR Code

Development of an Extraction Method of Cortical Surfaces from MR Images for Improvement in Efficiency and Accuracy

효율성과 정확도 향상을 위한 MR 영상에서의 뇌 외곽선 추출 기법 개발

  • An, Kwang-Ok (School of Electrical Engineering, Seoul National University) ;
  • Jung, Hyun-Kyo (School of Electrical Engineering, Seoul National University)
  • Published : 2007.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

In order to study cortical properties in human, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Among many approaches, surface-based method that reconstructs a 3-D model from contour lines on cross-section images is widely used. In general, however, medical brain imaging has some problems such as the complexity of the images, non-linear gain artifacts and so on. Due these limitations, therefore, extracting anatomical structures from imaging data is very a complicated and time-consuming task. In this paper, we present an improved method for extracting contour lines of cortical surface from magnetic resonance images that simplifies procedures of a conventional method. The conventional method obtains contour lines through thinning and chain code process. On the other hand, the proposed method can extract contour lines from comparison between boundary data and labeling image without supplementary processes. The usefulness of the proposed method has been verified using brain image.

Keywords

References

  1. M. Levoy, 'Display of surfaces from volume data,' IEEE Computer Graphics and Application, vol. 8, no. 3, pp. 29-37, 1988 https://doi.org/10.1109/38.511
  2. R. Yagel and A. Kaufman, 'Template-based volume viewing,' Computer Graphics Forum (Eurographics 92 Proceedings), pp. 153-167, 1992
  3. P. Lacroute and M. Levoy, 'Fast volume rendering using a shear-warp factorization of the viewing transformation,' Computer Graphics (SIGGRAPH 94 Proceedings), pp. 451-458, 1994
  4. D. MacDonald, N. Kabani, D. Avix, and A.C. Evans, 'Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI,' Neuroimage, pp. 340-356, 2000 https://doi.org/10.1006/nimg.1999.0534
  5. A.M. Dale, B. Fischl, and M.I. Sereno, 'Cortical surface-based analysis: segmentation and surface reconstruction,' NeuroImage, pp. 179-194, 1999
  6. G. Gerig, O. Kubler, R. Kiknis, and F.A. Jolesz, 'Nonlinear anisotropic filtering of MRI data,' IEEE Transactions on Medical Imaging, vol. 11, no. 2, pp. 221-232, 1992 https://doi.org/10.1109/42.141646
  7. A.A. Samsonov and C.R. Johnson, 'Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels,' Magnetic Resonance in Medicine, vol. 52, pp. 798-806, 2004 https://doi.org/10.1002/mrm.20207
  8. P.Y. Yin, and L.H. Chen, 'A fast iterative scheme for multilevel thresholding method,' Signal Processing, vol. 60, pp. 305-313, 1997 https://doi.org/10.1016/S0165-1684(97)00080-7
  9. P.Y. Yin, 'A fast scheme for optimal thresholding using genetic algorithm,' Signal Processing, vol. 72, pp. 85-95, 1999 https://doi.org/10.1016/S0165-1684(98)00167-4
  10. C. Arcelli, and G.S.D. Baja, 'A width-independent fast thinning algorithm,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 4, pp. 463-474, 1985 https://doi.org/10.1109/TPAMI.1985.4767685
  11. B.K. Jang and R.T. Chin, 'One-pass parallel thinning: analysis, properties, and quantitative evaluation,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 11, pp. 1129-1140, 1992 https://doi.org/10.1109/34.166630
  12. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, Publishing Company, Inc., 1993
  13. R. Jain, R. Kasturi, and B.G. Schunck, Machine Vision, McGraw-Hill, Inc., 1995