Tissue Image Segmentation

1. Tissue Image Segmentation - Presenter : Lin Yang - Advisor : Dr. David J. Foran - “A General Framework for Segmenting Imaged Pathology Specimens Using Level-set and Gaussian Hidden Markov Random Fields ”

2. Problem Statement • Image Segmentation • Region based method • Segmentation by clustering – mean shift • Segmentation by graph theory • Segmentation by MRFs, Gaussian Mixture Models and EM algorithm • Contour based method • Active contour models • Traditional KWT snake • GVF snake • Geodesic snake • Level – set based snake • Active contour without edge

3. The Choice of Filter Bank(1) • The Gabor filter bank • The Leung – Malik (LM) filter bank

4. The Choice of Filter Bank(2) • The Schmid filter bank • The Maximum Response (MR) filter bank

5. MRF Segmentation Model • Assume a set of observed (y) and hidden (x) random variables • fy represents the low-level features • ωx represents the labels of each pixel • Now the segmentation problem can be modeled as a MAP(maximum a posterior) estimation

6. Gibbs prior • Gibbs prior • Intuitive Understanding • Hammersley-Clifford theorem

7. Gaussian Mixture Model • Given feature f, the Gaussian Mixture Model is defined as follows:

8. Initialization and EM • Applying EM algorithm to get the MLE estimation of the parameters set W:

9. Complete Cost Function • The complete cost function combining the Gaussian mixture models and the Gibbs priors will have the following forms • Notice that the parameters are the results of EM algorithm

10. Optimization Algorithm (1) • Stochastic optimization • Simulate Annealing • Gibbs Sampling • Global Minimum • Algorithm • Code from Matlab

11. Optimization Algorithm (2)

12. Experimental Results(1) • Synthetic Image

13. Experimental Results(2) • Standard Texture Image

14. Level Set Based Active Contour • Traditional Snake • Topological change • Difficulty with initialization problem – GVF snake partially solve this problem • Level – Set or Geodesic Snake • Topology changes can be easily handled and initial positions are not sensitive • Computation is complex, speed is slow and the implementation is relatively difficult • Multiphase level-set framework – very fast • Snake with MRF • Apply snake on the likelihood map of MRF can mix the advantages of MRF and snake

15. Experimental Results(3)

16. Experimental Results(4)

17. Performance Evaluation • Features are more important than classification algorithm • Deformable Model • None of the gradient based or even region based deformable model alone works well in our real case • Gaussian Mixture Model • The result is not very good because it will over-segment the image • MRF based GMM will improve the result because the introduction of Gibbs prior • Clustering Based Segmentation • Actually provide satisfactory results for texture only segmentation • Has some problem with homogenous segmentation when combined with intensity information • Total unsupervised approach is very hard for our application

18. Pros and Cons • Advantages: • Actually perform very well for our application. • Can be combined with many different segmentation models • Still active field and even show up in CVPR 2005. • Disadvantages: • Speed, speed and speed • Hundreds of, if not thousands of, literatures are proposed for increasing the speed. • Matlab implementation and C/C++ implementation, big difference, the C++ implementation takes only no more than 1 minute for one image with 600*600 pixels • Gaussian Models are not always, if not never, hold for many medical image processing applications

19. Reference • Chad Carson, Serge Belongie, Hayit Greenspan and Jitendra Malik, “Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying, ” IEEE Tran. on Pattern Anal. and Mach. Intell., vol 24, no. 8, pp1027-1037 • C. Bouman and B. Liu, “Multiple Resolution Segmentation of Textured Images,'' IEEE Trans. on Pattern Anal. and Mach. Intell., vol. 13, no. 2, pp. 99-113, Feb. 1991. • C. A. Bouman and M. Shapiro, “A Multiscale Random Field Model for Bayesian Image Segmentation,'' IEEE Trans. on Image Processing, vol. 3, no. 2, pp. 162-177, March 1994 • R. O. Duda, P. E. Hart, and D. G. Stork, Patten Classification, 2nd Edition, Wiley, 2000. • David A. Forsyth and Jean Ponce, Computer Vision A Modern Approach, 1st Edition, Prentice Hall, 2003. • Mario A. T. Figueiredo, “Bayesian Image Segmentation Using Wavelet-Based Priors,” CVPR, vol. 1 pp 437-443, 2005. • R. Malladi, J. A. Sethian, B. C. Vemuri, "Shape Modeling with Front Propagation: A Level Set Approach," IEEE Trans. on Pattern Anal. and Mach. Intell., vol. 17 No. 2: 158-175, Feburary 1995. • T. F. Chan, L. A. Vese, "A Level Set Algorithm for Minimizing the Mumford-Shah Functional in Image Processing," Proceedings of the IEEE Workshop on Variational and Level Set Methods, pp. 161-171, 2001. • Y. Zhang, M. Brady, S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Transactions on Medical Imaging, Vol. 20, no 1, pp. 45 – 57, Jan 2001 • T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” International Journal of Computer Vision, 43(1):29-44, June 2001

20. Thank You