Andy: Update from Wiki

2006-12-18T13:17:22Z

Update from Wiki

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= BAYESIAN CLASSIFIER IMAGE FILTER =

[[Image:BayesianProgWeekProject.ppt|Link to Project Template]] 

== Introduction ==

Utilizing Bayes's Rule along with an edge-preserving, affine invariant noise remover, brain imagery can be successfully segmented.

== Use Case ==

I'd like to segment a brain image or 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 segmented data image or volume with 'N' classes.

== Data ==

We are using the 28 UCI Schizo cases (structural) for this project. [https://portal.nbirn.net/BIRN/cgi-bin/DataGrid/browse.cgi?browseloc=/home/Projects/Study5000__0005/Files/Archive data].

== Algorithm ==

# The user decides the number of distinct classes in the data: 'N' (default, N = 2)
# Generate 'N' prior images (default, 'N' uniform prior images) 
# Generate 'N' statistical distributions (default, 'N' normal distributions) 
# Generate 'N' data images by applying the statistical distributions to the raw data 
# Generate 'N' posterior images by applying Bayes' rule to the prior and data images 
# Smooth the posterior images for 'm' iterations using an anisotropic PDE and renormalize after each iteration (default, m = 5) 
# Apply maximum a posteriori rule to apply labeling and finalize segmentation

== Porting the Code to an ITK filter ==

The following are ideas per our 06/Sep/2005 project TCON:

* Let's subdivide the code into 3 classes:

# An initializer class
# A generic Bayesian classifier class
# A Bayesian classifier with posterior smoothing class

* Filter data inputs include:

# The raw data volume (required, restricted to scalar images)
# 'N' prior images (optional) {default, 'N' uniform prior images}
# 'N' statistical distributions (optional) {default, 'N' normal distributions}
# A segmentation mask (optional) {default, no mask}

* Filter control inputs include:

# Number of Classes 'N' (optional) {default, N = 2}
# Number of posterior smoothing iterations 'm' (optional) {default, m = 5}

* A wish list of future control inputs include:

# Other smoothing parameters

* Filter data outputs include:

# The resulting labelmap segmentation
# The intermediate posterior maps (optional)

== Project Status ==

* Fully implemented and tested in Matlab and in ITK. 
* The working ITK code is currently being ported to an ITK filter for inclusion in the CVS repository. 
* The working ITK code has been committed to the [http://www.na-mic.org:8000/svn/NAMICSandBox/BayesianSegmentationModule/ SandBox].

== Publications ==

* Algorithm details currently in submission to SPIE.
* ITK implementation published in the Insight Journal.

== Contacts ==

* [[User:Melonakos|John Melonakos]] @ Georgia Tech
* Luis Ibanez @ Kitware

Progress Report:Bayesian Classification - Revision history

Andy: Update from Wiki