tissue image segmentation
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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 ”
![Page 2: Tissue Image Segmentation](https://reader031.vdocuments.mx/reader031/viewer/2022012918/56813760550346895d9eef4f/html5/thumbnails/2.jpg)
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
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The Choice of Filter Bank(1)
The Gabor filter bank
The Leung – Malik (LM) filter bank
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The Choice of Filter Bank(2)
The Schmid filter bank
The Maximum Response (MR) filter bank
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MRF Segmentation Model
Assume a set of observed (y) and hidden (x) random variables
fy represents the low-level features ωx represents the labels of each pixel Now the segmentation problem can be
modeled as a MAP(maximum a posterior) estimation
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Gibbs prior
Gibbs prior
Intuitive Understanding
Hammersley-Clifford theorem
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Gaussian Mixture Model
Given feature f, the Gaussian Mixture Model is defined as follows:
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Initialization and EM
Applying EM algorithm to get the MLE estimation of the parameters set W:
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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
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Optimization Algorithm (1)
Stochastic optimization Simulate Annealing
• Gibbs Sampling • Global Minimum
Algorithm• Code from Matlab
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Optimization Algorithm (2)
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Experimental Results(1)
Synthetic Image
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Experimental Results(2)
Standard Texture Image
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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
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Experimental Results(3)
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Experimental Results(4)
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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
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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
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Reference1. 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
2. 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.
3. 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
4. R. O. Duda, P. E. Hart, and D. G. Stork, Patten Classification, 2nd Edition, Wiley, 2000.
5. David A. Forsyth and Jean Ponce, Computer Vision A Modern Approach, 1st Edition, Prentice Hall, 2003.
6. Mario A. T. Figueiredo, “Bayesian Image Segmentation Using Wavelet-Based Priors,” CVPR, vol. 1 pp 437-443, 2005.
7. 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.
8. 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.
9. 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
10. 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
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Thank You