Image Segmentation Using Level Set Method with New Speed Function

새로운 속도함수를 갖는 레벨 셋 방법을 이용한 의료영상분할

Kim, Sun-Worl;Cho, Wan-Hyun

  • Received : 20100400
  • Accepted : 20110300
  • Published : 2011.04.30


In this paper, we propose a new hybrid speed function for image segmentation using level set. A new proposed speed function uses the region and boundary information of image object for the exact result of segmentation. The region information is defined by the probability information of pixel intensity in a ROI(region-of-interest), and the boundary information is defined by the gradient vector flow obtained from the gradient of image. We show the results of experiment for an various artificial image and real medical image to verify the accuracy of segmentation using proposed method.


Medical image segmentation;level set method;hybrid speed function;gradient vector flow


  1. Chan, T. F. and Vese, L. A. (2001). Active contours without edges, IEEE Transactions on Image Processing, 10, 266-277.
  2. Chuang, C. H. and Lie, W. N. (2004). A downstream algorithm based on extended gradient vector flow field for object segmentation, IEEE Transactions On Image Processing, 12, 1379-1392.
  3. Jayadevappa, D., Srinivas, K. S. and Murty, D. S. (2009). A new deformable model based on level sets for medical image segmentation, IAENG International Journal of Computer Science, 36.
  4. Loog, M. and Ginneken, B. V. (2006). Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification, IEEE Transactions on Medical Imaging, 25, 602-611.
  5. Osher, S. and Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, 79, 12-49.
  6. Rousson, M. and Deriche, R. (2002). Variational framework for active and adaptive segmentation of vector valued images, Proceeding of IEEE Workshop on Motion and Video Computing.
  7. Sethian, J. A. (1999). Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd Edition, Cambridge University Press.
  8. Xu, C. and Prince, J. (1995). Snake, shapes, and gradient vector flow, IEEE Transactions On Image Processing, 7, 359-369.
  9. Xu, C. and Prince, J. (1997). Gradient vector ow: A new external force for snake, Proceeding of IEEE Computer Society Conference On Computer Vision And Pattern Recognition.


Supported by : 한국연구재단