Difference between revisions of "Projects/Structural/2007 Project Week Nonrigid Groupwise Registration"

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{|
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|[[Image:ProjectWeek-2007.png|thumb|left|320px|Return to [[2007_Programming/Project_Week_MIT|Project Week Main Page]] ]]
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<table width="420" border="1" align="center" >
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        <caption>
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        </caption>
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        <tr>
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          <td width="210" align="center"> [[Image:MeanSliceSerdar.jpg|thumb|center|Mean Image before registration]]  </td>
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          <td width="210" align="center"> [[Image:MeanSlice1Serdar.jpg|thumb|center|Mean Image after registration]]  </td>
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        </tr>
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        <tr>
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          <td width="210" align="center"> [[Image:STDSliceSerdar.jpg|thumb|center|STD Image before registration]] </td>
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          <td width="210" align="center"> [[Image:STDSlice1Serdar.jpg|thumb|center|STD Image after registration]] </td>
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        </tr>
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        <tr>
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          <td colspan="2"> Registration results on a synthetic dataset of 30 MR images. <td>
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        </tr>
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</table>
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|}
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__NOTOC__
 
===Key Investigators===
 
===Key Investigators===
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* MIT: Serdar K Balci, Polina Golland
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* MIT: Serdar K Balci, Polina Golland, Lilla Zollei, Sandy Wells
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* Kitware: Louis Ibanez
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* Kitware: Luis Ibanez
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<div style="margin: 20px;">
 
<div style="margin: 20px;">
  
 
<div style="width: 27%; float: left; padding-right: 3%;">
 
<div style="width: 27%; float: left; padding-right: 3%;">
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<h1>Objective</h1>
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We extended a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines.
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The objective in groupwise registration is to bring subjects in a population into joint alignment in order to establish correspondences among anatomical structures.
  
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</div>
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<div style="width: 27%; float: left; padding-right: 3%;">
  
 
<h1>Approach, Plan</h1>
 
<h1>Approach, Plan</h1>
  
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Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below.  The main challenge to this approach is <foo>.
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* Implement a non-rigid groupwise registration method
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** Joint alignment of images
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** Minimize sum of univariate entropies
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** Efficient implementation using multi-threaded classes.
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* B-splines as the non-rigid deformation model
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** Optimize ITK's B-Spline implementation by computing jacobian field locally
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*Extended ITK's pairwise registration framework to groupwise registration
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*To compare groupwise registration to pairwise approaches
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** Implement a template based method where every subject is registered to the mean image using sum of squared differences.
  
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Our plan for the project week is to first try out <bar>,...
 
 
</div>
 
</div>
  
 
<div style="width: 40%; float: left;">
 
<div style="width: 40%; float: left;">
  
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<h1>Objective</h1>
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<h1>Progress</h1>
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We are developing methods for groupwise registration of medical images.
 
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The goal is to simultaneously register a collection of images.
 
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Our project is already implemented in ITK, in this workshop we achieved to
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*Submit the project to ITK's Namic SANDBOX repositories [http://www.na-mic.org/svn/NAMICSandBox/trunk/MultiImageRegistration/ link to code]
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*Provide test cases using ITK's Brainweb data [http://public.kitware.com/pub/itk/Data/BrainWeb/BrainPart2.tgz  link to data]
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*Submit test results to the dashboard
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*Make modifications to the source code for memory efficiency
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*Build a package for Insight Journal submission (in progress)
 
</div>
 
</div>
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<div style="width: 27%; float: left; padding-right: 3%;">
 

Latest revision as of 14:11, 29 June 2007

Home < Projects < Structural < 2007 Project Week Nonrigid Groupwise Registration
Return to Project Week Main Page
Mean Image before registration
Mean Image after registration
STD Image before registration
STD Image after registration
Registration results on a synthetic dataset of 30 MR images.



Key Investigators

  • MIT: Serdar K Balci, Polina Golland, Lilla Zollei, Sandy Wells
  • Kitware: Luis Ibanez

Objective

We extended a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines.

The objective in groupwise registration is to bring subjects in a population into joint alignment in order to establish correspondences among anatomical structures.


Approach, Plan

  • Implement a non-rigid groupwise registration method
    • Joint alignment of images
    • Minimize sum of univariate entropies
    • Efficient implementation using multi-threaded classes.
  • B-splines as the non-rigid deformation model
    • Optimize ITK's B-Spline implementation by computing jacobian field locally
  • Extended ITK's pairwise registration framework to groupwise registration
  • To compare groupwise registration to pairwise approaches
    • Implement a template based method where every subject is registered to the mean image using sum of squared differences.

Progress

Our project is already implemented in ITK, in this workshop we achieved to

  • Submit the project to ITK's Namic SANDBOX repositories link to code
  • Provide test cases using ITK's Brainweb data link to data
  • Submit test results to the dashboard
  • Make modifications to the source code for memory efficiency
  • Build a package for Insight Journal submission (in progress)
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