Projects:KnowledgeBasedBayesianSegmentation

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Objective:

This ITK filter is a segmentation algorithm which utilizes Bayes's Rule along with an affine-invarient anisotropic smoothing filter.

Progress:

Use Case

I'd like to segment a volume or sub-volume into 'N' classes in a very general manner. I will provide the data and the number of classes that I expect and the algorithm will output a labelmap with 'N' classes.

Data

We are using the Harvard structural datasets.

Algorithm

1. The user sets the number of distinct classes for segmentation: 'N'
2. Generate 'N' prior images (default, 'N' uniform prior images)
   
3. Generate 'N' statistical distributions (default, 'N' normal distributions)
   
4. Generate 'N' membership images by applying the statistical distributions to the raw data
   
5. Generate 'N' posterior images by applying Bayes' rule to the prior and membership images
   
6. Smooth the posterior images for 'm' iterations using an affine-invarient anisotropic smoothing filter and renormalize after each iteration (default, m = 5)
   
7. Apply maximum a posteriori rule to apply labeling and finalize segmentation

The ITK filter design

Some Results

• Raw Image
• Manual Labels
• Filter Output

Project Status

• Fully incorporated into itkBayesianClassificationImageFilter and itkBayesianClassificationInitializationImageFilter in ITK CVS.
  
• Fully wrapped in VTK for use in Slicer.
  
• The working ITK code has been committed to the SandBox

References:

• J. Melonakos, K. Krishnan, and A. Tannenbaum. An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter. Insight Journal, 2006.
• J. Melonakos, R. Al-Hakim, J. Fallon, and A. Tannenbaum. Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit. Insight Journal, 2005.

Key Investigators:

• John Melonakos @ Georgia Tech
• Yi Gao @ Georgia Tech
• Allen Tannenbaum @ Georgia Tech
• Luis Ibanez @ Kitware
• Karthik Krishnan @ Kitware

Links:

• Georgia Tech Algorithms
• NA-MIC Collaborations