정보처리학회논문지A (The KIPS Transactions:PartA)

한국정보처리학회 (Korea Information Processing Society)
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표면 검출과 볼륨 확장을 이용한 삼차원 물체의 선택 분할

Selective Segmentation of 3-D Objects Using Surface Detection and Volume Growing

  • 배소영 (이화여자대학교 대학원 컴퓨터학과) ;
  • 최수미 (이화여자대학교 컴퓨터 그래픽스/가상현실 연구센터) ;
  • 최유주 (이화여자대학교 대학원 컴퓨터학과) ;
  • 김명희 (이화여자대학교 컴퓨터학과)
  • Bae, So-Young (Dept.of Computer, Graduate School of Ewah Womans University) ;
  • Choi, Soo-Mi (Dept.of Computer Graphic, Ewah Womans University) ;
  • Choi, Yoo-Joo (Dept.of Computer, Graduate School of Ewah Womans University) ;
  • Kim, Myoung-Hee (Dept.of Computer, Ewah Womans University)
  • 발행 : 2002.03.01
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초록

삼차원 볼륨 영상으로부터 대상 물체를 분할하는 것은 가시화 또는 볼륨 측정을 위해서 매우 중요한 단계이다. 본 논문에서는 볼륨 가시화를 위해 널리 사용되는 르보이 필터링 방법을 개선하여 물체의 표면을 검출하는 방법을 제시한다. 그리고 형태학적 연산자를 이용하여 완전히 닫힌 표면을 생성하고 볼륨 확장 알고리즘에 의해 물체를 선택적으로 분할한다. 제시된 방법은 합성된 삼차원 구 영상과 심혈관 조영영상에 적용되었다. 이 방법을 합성된 구 영상을 사용하여 기존의 브로이 필터링과 정량적으로 비교한 결과 제시한 방법이 복셀 오차면에서 더 우수하였다. 또한 심혈관 영상을 사용하여 시각적으로 비교한 결과 역시 제시한 방법이 더 정확하였다. 본 논문에서 제시한 방법은 삼차원 영상처리에서 자주 함께 사용되는 분할, 가시화, 측정을 쉽게 연계할 수 있기 때문에 볼륨 영상의 분할을 위해 매우 효고적이다.

The segmentation of target objects from three dimensional volume images is an essential step for visualization and volume measurement. In this paper, we present a method to detect the surface of objects by improving the widely used levoy filtering for volume visualization. Using morphological operators we generate completely closed surfaces and selectively segment objects using the volume growing algorithm. The presented method was applied to 3-D artificial sphere images and angiocardiograms. We quantitatively compared this method with the conventional levoy filtering using artificial sphereimages, and the results showed that our method is better in the aspect of voxel errors. The results of visual comparison using angiocardiograms also showed that our method is more accurate. The presented method in this paper is very effective for segmentation of volume data because segmentation, visualization and measurement are frequently used together for 3-D image processing and they can be easily related in our method.

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참고문헌

  1. J. S. Duncan and N. Ayache, 'Medical Image Analysis : Progress over Two Decades and the Challenges Ahead,' IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.22, No.1, pp.85-106, Jan. 2000 https://doi.org/10.1109/34.824822
  2. S. M. Choi, J. E. Lee, J. Kim and M. H. Kim, 'Volumetric Object Reconstruction Using the 3D-MRF Model-Based Segmentation,' IEEE Transactions on Medical Imaging Vol.16, No.6, pp.887-892, Dec. 1997 https://doi.org/10.1109/42.650884
  3. J. Park, M. Kim, T. Mclnerney and D. Terzopoulos, 'A Multiscale Deformable Model for Extracting Complex Surfaces from Volume Images,' Proceedings of Pacific Graphics '99, pp.208-215, Oct. 1999 https://doi.org/10.1109/PCCGA.1999.803364
  4. T. Mclnerney and D. Terzopoulos, 'T-snakes : Topology adaptive snakes,' Medical Image Analysis Vol.4, pp.73-91, 2000 https://doi.org/10.1016/S1361-8415(00)00008-6
  5. R. Shekhar, R. M. Cothren, D. G. Vince, S. Chandra, J. D. Thomas and J. F. Cornhill, 'Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images,' Computerized Medical Imaging and Graphics Vol.23, No.6, pp.299-309, Dec. 1999 https://doi.org/10.1016/S0895-6111(99)00029-4
  6. M. Levoy, 'Display of surfaces from volume data,' IEEE Computer Graphics and Applications, pp.29-37, May, 1988 https://doi.org/10.1109/38.511
  7. T. A. Buck, H. Ehricke, W. Strasser and L. Thutfjell, '3-D segmentation of medical structures by integration of raycasting with anatomic knowledge,' Computers & Graphics Vol.19, No.3, pp.441-449, 1995 https://doi.org/10.1016/0097-8493(95)00014-4
  8. M. N. Ahmed and A. A. Farag, '3D Segmentation and Labeling Using Unsupervised Clustering for Volumetric Measurements on Brain CT Imaging,' proceedings of ANNIE '96, 1996
  9. M. Bomans, K. H. Hoehne, U. Tiede and M. Riemer, '3-D Segmentation of MR Images of the Head for 3-D Display,' IEEE Transactions on Medical Imaging Vol.9, No.2, pp.177-183, Jun. 1990 https://doi.org/10.1109/42.56342
  10. K. H. Hoene and W. A. Hanson, 'Interactive 3D Segmentation of MRI and CT Volumes using Morphological Operations,' Journal of Computer Assisted Tomography, Vol.16, No.2, pp.285-294, 1992 https://doi.org/10.1097/00004728-199203000-00019
  11. S. W. Zucker and R. A. Hummel, 'A three-dimensional edge operator,' IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.3, pp.324-331, May, 1981 https://doi.org/10.1109/TPAMI.1981.4767105
  12. P. S. P. Wang and Y. Y. Zhang, 'A Fast and Flexible Thinning Algorithm,' IEEE Transactions on Computers Vol.38, No.5, pp.741-745, 1989 https://doi.org/10.1109/12.24276
  13. J. Jaeger, '3D HeartView,' Computer Graphik Topics(CG Topics), Report of the INI-GraphicsNet Vol. 4, pp. 6-7, 1997
  14. R. Westermann and T. Ertl, 'A Multiscale Approach to Integrated Volume Segmentation and Rendering,' Proceedings of EUROGRAPHICS '97, pp.117-129, 1997
  15. J. Sijbers, M. Verhoye, P. Scheunders, A. V. Linden, D. V. Dyck, and E. Raman, 'Watershed based segmentation of 3D MR data for volume quantization,' Magnetic Resonance Imaging Vol.15, No.6, pp.679-688, 1997 https://doi.org/10.1016/S0730-725X(97)00033-7
  16. X. Yu and J. yla-Jaaski, 'Interactive Surface Segmentation for Medical Images,' Proceedings of SPIE on Application of Digital Image Processing Vol.2564, pp.519-527, 1995