1 / 20

Tissue Image Segmentation

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 ”. Problem Statement. Image Segmentation Region based method

linda-brady
Download Presentation

Tissue Image Segmentation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related