Advertisement

Segmentation for Medical Image Using a Statistical Initial Process and a Level Set Method

  • WanHyun Cho
  • SangCheol Park
  • MyungEun Lee
  • SoonYoung Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)

Abstract

In this paper, we present a segmentation method for medical image based on the statistical clustering technique and the level set method. The segmentation method consists of a pre-processing stage for initialization and the final segmentation stage. First, in the initial segmentation stage, we adopt the Gaussian mixture model (GMM) and the Deterministic Annealing Expectation Maximization (DAEM) algorithm to compute the posterior probabilities for each pixels belonging to some region. And then we usually segment an image to assign each pixel to the object with maximum posterior probability. Next, we use the level set method to achieve the final segmentation. By using the level set method with a new defined speed function, the segmentation accuracy can be improved while making the boundaries of each object much smoother. This function combines the alignment term, which makes a level set as close as possible to a boundary of object, the minimal variance term, which best separates the interior and exterior in the contour and the mean curvature term, which makes a segmented boundary become less sensitive to noise. And we also use the Fast Matching Method for re-initialization that can reduce the computing time largely. The experimental results show that our proposed method can segment exactly the synthetic and CT images.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Armato, S.G., Sensakovic, W.F.: Automated lung Segmentation for Thoracic CT. Academic Radiology 11, 1011–1021 (2004)CrossRefGoogle Scholar
  2. 2.
    Itai, Y., et al.: Automatic extraction of abnormal areas on CT images of the lung area. In: International Symposium on Advanced Intelligent Systems, Korea, vol. 1, pp. 360–392 (2005)Google Scholar
  3. 3.
    Kimmel, R.: Fast Edge Integration, Geometric Level Set Methods, pp. 59–77. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. In: Proceedings of ICCV 1995, Boston, USA, pp. 694–699 (1995)Google Scholar
  5. 5.
    Malladi, R., Setian, J.A., Vemuri, B.C.: Shape Modeling with Front Propagation: A level Set Approach. IEEE Transaction on PAMI 17, 158–175 (1995)Google Scholar
  6. 6.
    Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation. Technical Report JHU/ECE 99-01, The Johns Hopkins University (1998)Google Scholar
  7. 7.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods. Cambridge university press, Cambridge (2005)Google Scholar
  8. 8.
    Sonka, M., Fitzpatick, J.M.: Handbook of Medical imaging: Medical Image Processing and Analysis, vol. 2. SPIE Press, San Jose (2000)CrossRefGoogle Scholar
  9. 9.
    Vese, L.A., Chan, T.F.: A Multiphase Level Set Framework for Image Segmentation Using the Mumford and shah model. International Journal of Computer Vision 50, 271–293 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Whitaker, R.: Modeling deformable Surfaces with Level Sets. IEEE Computer Graphics and Applications 24(5), 6–9 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • WanHyun Cho
    • 1
  • SangCheol Park
    • 1
  • MyungEun Lee
    • 2
  • SoonYoung Park
    • 2
  1. 1.Department of StatisticsChonnam National UniversityKorea
  2. 2.Department of Electronics EngineeringMokpo National UniversityKorea

Personalised recommendations