# Difference between revisions of "Projects:KnowledgeBasedBayesianSegmentation"

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= Knowledge Based Bayesian Segmentation = | = Knowledge Based Bayesian Segmentation = |

## Revision as of 03:24, 6 January 2008

Home < Projects:KnowledgeBasedBayesianSegmentationBack to NA-MIC Collaborations, Georgia Tech Algorithms, Kitware Engineering

# Knowledge Based Bayesian Segmentation

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

# Description

*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 have applied this algorithm to 20 normal brain MRI data-sets. We used publicly available data-sets from the Internet Brain Segmentation Repository (IBSR) offered by the Massachusetts General Hospital, Center for Morphometric Analysis. The IBSR data-sets are T1-weighted, 3D coronal brain scans after having been positionally normalized. Manual expert segmentations for these data-sets are publicly available and represent the ground truth used in this work.

*Algorithm*

This algorithm can be cast in either a static or dynamic framework. In the static framework, the following is the algorithm:

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

In the dynamic framework, the following image depicts the adaptation of the static framework to the dynamic formulation:

*The ITK filter design*

*Some Results*

*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

# Key Investigators

- Georgia Tech Algorithms: John Melonakos, Yi Gao, Allen Tannenbaum
- Kitware Engineering: Luis Ibanez, Karthik Krishnan

# Publications

*In Print*

*In Press*

- J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.