Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Segmentation of tooth using Adaptive Optimal Thresholding and B-spline Fitting in CT image slices

적응 최적 임계화와 B-spline 적합을 사용한 CT영상열내 치아 분할

  • Heo, Hoon (Department of Computer Engineering, Graduate School, KyungHee University) ;
  • Chae, Ok-Sam (Department of Computer Engineering, KyungHee University)
  • 허훈 (경희대학교 일반대학교 컴퓨터공학과) ;
  • 채옥삼 (경희대학교 컴퓨터공학과)
  • Published : 2004.07.01
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Abstract

In the dental field, the 3D tooth model in which each tooth can be manipulated individually is an essential component for the simulation of orthodontic surgery and treatment. To reconstruct such a tooth model from CT slices, we need to define the accurate boundary of each tooth from CT slices. However, the global threshold method, which is commonly used in most existing 3D reconstruction systems, is not effective for the tooth segmentation in the CT image. In tooth CT slices, some teeth touch with other teeth and some are located inside of alveolar bone whose intensity is similar to that of teeth. In this paper, we propose an image segmentation algorithm based on B-spline curve fitting to produce smooth tooth regions from such CT slices. The proposed algorithm prevents the malfitting problem of the B-spline algorithm by providing accurate initial tooth boundary for the fitting process. This paper proposes an optimal threshold scheme using the intensity and shape information passed by previous slice for the initial boundary generation and an efficient B-spline fitting method based on genetic algorithm. The test result shows that the proposed method detects contour of the individual tooth successfully and can produce a smooth and accurate 3D tooth model for the simulation of orthodontic surgery and treatment.

치과 분야에서 치아가 개별적으로 조작될 수 있는 3차원 치아 모델은 치과 치료와 시술 시뮬레이션을 위해 중요한 요소이다. CT영상으로부터 이러한 치아 모델을 재구성하기 위해서는 각 치아의 경계를 정확하게 분할할 수 있어야 한다. 그러나 기존의 3차원 재구성 시스템에서 사용되는 임계화 방법은 치아들과 치아와 비슷한 밝기의 치조골이 서로 인접해서 나타나는 CT 영상열에서 효율적이지 못하다. 본 논문에서는 CT영상에서 부드러운 치아 경계를 추출하기 위해 B-spline 곡선 적합을 이용한 치아 분할 방법을 제안한다. 성공적인 적합을 위해서 이전 슬라이스의 분할정보와 적응 최적 임계화 방법을 기반으로 한 초기경계 생성방법을 제안한다. 그리고 적합과정에서 이웃한 유사한 물체에 적합되는 것을 줄일 수 있는 유전자 알고리즘을 이용한 B-spline 적합방법을 제안한다. 평가결과 제안된 알고리즘은 개별치아의 경계를 성공적으로 검출하였으며 이를 이용하여 시술과 치료 과정의 시뮬레이션을 위한 치아의 3차원 모델을 정확하고 부드럽게 생성할 수 있음을 보였다.

Keywords

References

  1. J. H. Ryu, H. S. Kim, K. H. Lee, 'Contour based algorithms for generating 3D medical models,' Scanning Congress 2001: Numerization 3D session, Paris, France, April 4-5,2001
  2. G. Bohm, C. Knoll, V. G. Colomer, M. Alcaniz-Raya, S. Albalat, 'Three-dimensional segmentation of bone structures in CT images,' SPIE, vol.3661,pp.277-286, San Diego, California, 2,1999 https://doi.org/10.1117/12.348582
  3. M. Kass, A. Witkin and D. Terzopoulos 'Snakes, active contour models,' International Journal of Computer Vision, Vol.1, 1987, pp.321-331 https://doi.org/10.1007/BF00133570
  4. L. Ballerni, L. Bocchi, 'Multiple genetic snakes for bone segmentation,' EvoWorkshops 2003, LNCS 2611, pp. 346-356, 2003
  5. Toet, A., Hajema, W. P., 'Genetic contour matching,' Pattern Recognition Letters 16, pp.849-856, 1995 https://doi.org/10.1016/0167-8655(95)00015-9
  6. Liu, S., Ma, M., 'Seed-growing segmentation of 3D surfaces from CT-contour data,' Computer-Aided Design vol. 31, pp.517-536, 1999 https://doi.org/10.1016/S0010-4485(99)00050-0
  7. Brigger, P., Hoeg, J., Unser, M., 'B-spline snakes: A flexible tool for parametric contour detection,' IEEE Trans. on Image Processing, vol. 9, no. 9, pp.1484-1496, 2000 https://doi.org/10.1109/83.862624
  8. G. Farin, Curves and surfaces for CAGD, Academic Press, California, 1997
  9. E. K. P. Chong, S. H. Zak, An introduction to optimization, Wiley Interscience, New York, 2001
  10. Zijdenbos, A.P., Dawant, B. M., Margolin, R. A, Palmer, A C., 'Morphometric analysis of white matter lesions in MR images: Method and validation,' IEEE Trans. Medical Imaging, vol. 13, pp.716-724, 1994 https://doi.org/10.1109/42.363096
  11. K. Bilger, J. Kupferschlager, W. Muller-Schauenburg, F. Nusslin, R. Bares, 'Threshold calculation for segmented attenuation correction in PET with histogram fitting,' IEEE Trans. on nuclear science, vol. 48, no. 1,pp.43-50, 2001 https://doi.org/10.1109/23.910831
  12. MacEachern, L. A., Manku, T., 'Genetic algorithms for active contour optimization,' IEEE Int. Sym. for Circuits and Systems, pp.229-232, 1998 https://doi.org/10.1109/ISCAS.1998.698801
  13. Ooi, C., Liatsis, P., 'Co-evolutionary-based active contour models in tracking of moving obstacles,' ADAS., IEEE Conf., pp.58-62, 2001 https://doi.org/10.1049/cp:20010499