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

• 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

The Choice of Filter Bank(1)

The Gabor filter bank

The Leung – Malik (LM) filter bank

The Choice of Filter Bank(2)

The Schmid filter bank

The Maximum Response (MR) filter bank

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

Gibbs prior

Gibbs prior

Intuitive Understanding

Hammersley-Clifford theorem

Gaussian Mixture Model

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

Initialization and EM

Applying EM algorithm to get the MLE estimation of the parameters set W:

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

Optimization Algorithm (1)

Stochastic optimization Simulate Annealing

• Gibbs Sampling • Global Minimum

Algorithm• Code from Matlab

Optimization Algorithm (2)

Experimental Results(1)

Synthetic Image

Experimental Results(2)

Standard Texture Image

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

Experimental Results(3)

Experimental Results(4)

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

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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Thank You