한국정보과학회논문지:소프트웨어및응용 (Journal of KIISE:Software and Applications)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

Snake 모델에서의 개선된 Gradient Vector Flow: 캡쳐 영역의 확장과 요면으로의 빠른 진행

Enhanced Gradient Vector Flow in the Snake Model: Extension of Capture Range and Fast Progress into Concavity

  • 발행 : 2006.01.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Gradient vector flow(GVF) snake 또는 active contour 모델은 영상 분할에서 훌륭한 성능을 보여준다. 그러나 기존의 snake 모델에는 제한된 캡쳐 영역과 요면으로의 느린 진행과 같은 문제점들이 존재한다. 본 논문은 주변의 필드로부터 외부장(external force field)을 확장시키고 변형된 평탄화기법을 이용하여 확장된 필드를 정규화 함으로서 GVF snake 모델의 성능을 개선시키는 새로운 방법을 제시한다. 시뮬레이션을 위해 사용된 U자 모양 이미지에서의 결과는 제안된 방법이 좀 더 큰 캡쳐 영역을 갖고 기존의 GVF snake 모델에 비하여 요면으로 빠르게 진행하는 것이 가능함을 보여준다.

The Gradient Vector Flow (GVF) snake or active contour model offers the best performance for image segmentation. However, there are problems in classical snake models such as the limited capture range and the slow progress into concavity. This paper presents a new method for enhancing the performance of the GVF snake model by extending the external force fields from the neighboring fields and using a modified smoothing method to regularize them. The results on a simulated U-shaped image showed that the proposed method has larger capture range and makes it possible for the contour to progress into concavity more quickly compared with the conventional GVF snake model.

키워드

참고문헌

  1. M. Kass, A. Witkin, and D. Terzopoulos, 'Snakes: Active contour models,' Int. J. Comput. Vis., vol. 1, pp. 321-331, 1987 https://doi.org/10.1007/BF00133570
  2. F. Leymarie and M. D. Levine, 'Tracking deformable objects in the plane using an active contour model,' IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp. 617-634, 1993 https://doi.org/10.1109/34.216733
  3. D. Terzopoulos and R Szeliski, 'Tracking with Kalman snakes,' in Active Vision, A. Blake and A. Yuille, Eds. Cambridge, MA: MIT Press, 1992, pp. 3-20
  4. W. E. L. Grimson, From Images to Surfaces: A computational study of the Human Early vision system. Cambridge, MA: The MIT Press, 1981
  5. D. Terzopoulos and K. Fleischer, 'Deformable models,' Vis. Comput., vol. 4, pp. 306-331, 1988 https://doi.org/10.1007/BF01908877
  6. V. Caselles, F. Catte, T. Call, and F. Dibos, 'A geometric model for active contours,' Numer. ？Math, vol. 66, pp. 1-31, 1993 https://doi.org/10.1007/BF01385685
  7. R. Malladi, J. A. Sethian, and B. C. Vemuri, 'Shape modeling with front propagation: A level set approach,' IEEE Trans. Pattern Anal. Machine Intell., vol. 17, pp. 158-175, 1995 https://doi.org/10.1109/34.368173
  8. V. Caselles, R. Kimmel, and G. Sapiro, 'Geodesic active contours,' in Proc. 5th Int. Conf. Computer Vision, 1995, pp. 694-699
  9. B. Leroy, I. Herlin, and L. D. Cohen, 'Multiresolution algorithms for active contour models,' in 12th Int. Conf. Analysis and Optimization of Systems, 1996, pp. 58-65
  10. L. D. Cohen, 'On active contour models and balloons,' CVGIP: Image Understand., vol. 53, pp. 211 -218, Mar. 1991 https://doi.org/10.1016/1049-9660(91)90028-N
  11. L. D. Cohen and I. Cohen, 'Finite-element methods for active contour models and balloons for 2-D and 3-D images,' IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp. 1131-1147, Nov. 1993 https://doi.org/10.1109/34.244675
  12. Xu. Chcnyang, and J. L. Prince, 'Snake, Shapes, and Gradient Vector Flow,' IEEE Transactions on Image Processing, vol. 7, no. 3. Mar. 1998 https://doi.org/10.1109/83.661186
  13. Xu. Chenyang, and J. L. Prince, 'Generallized Gradient Vector Flow External Forces for Active Contours,' Signal Processing, vol. 71, issue. 2, Dec., pp. 131-139, 1998 https://doi.org/10.1016/S0165-1684(98)00140-6
  14. Z. Yu and C. Bajai, 'Image Segmentation Using Gradient Vector Diffusion and Region Merging,' Proceedings of the 16th International Conference on Patrern Recognition, vol. 2, pp. 941-944, Aug 2002 https://doi.org/10.1109/ICPR.2002.1048460
  15. Dan Yuan and Siwei Lu, 'Simulated Static Electric FieJd(SSEF) Snake for Deformation Models,' Proceedings. 16th International Coriference on Pattern Recognition, vol. 1, pp. 11-15, Aug 2002
  16. I.H. Cho, J.S. Oh, K.S. Om, I.C. Song, K.H. Chang and D.S. Jeong, 'Preprocessing Effect by Using k-means Clustering and Merging Algorithms in MR Cardiac Left Ventricle Segmentation,' journal of Biomedical Engineering Research, vol. 24, pp. 55-60, Apr. 2003