Difference between revisions of "2015 Summer Project Week:LinearFeatureRegistration"

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* Matthew Holden
 
* Matthew Holden
 
* Nicole Aucoin, BWH (fiducials)
 
* Nicole Aucoin, BWH (fiducials)
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* Eugenio Marinetto, UC3M Madrid (Line-Based Registration, Reg.  Algorithm)
  
 
==Project Description==
 
==Project Description==
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<h3>Progress</h3>
 
<h3>Progress</h3>
 
* See: [https://github.com/mholden8/LinearObjectRegistration LinearObjectRegistration]
 
* See: [https://github.com/mholden8/LinearObjectRegistration LinearObjectRegistration]
 +
* Discussed generic module for collecting/defining/acquiring parametric features:
 +
** Use data structure to store a set of 0D/1D/2D parametric features
 +
** For each feature, store raw point data, as well as basic information (i.e. centroid, variance, etc.)
 +
** Individual registration algorithms are implemented as modules using the data structure
 +
** Individual algorithm is responsible for converting 0D/1D/2D data to points/lines/planes (if applicable)
 +
** Methods for acquisition: tracking, models, fiducials, segmentation
 +
* Use cases:
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** Phantom registration for ultrasound calibration
 +
** Line feature based image to tracker registration
 +
** Fiducial frame registration
 +
* Began implementation of generic parametric features
 
</div>
 
</div>
 
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Latest revision as of 14:46, 24 June 2015

Home < 2015 Summer Project Week:LinearFeatureRegistration

Key Investigators

  • Matthew Holden
  • Nicole Aucoin, BWH (fiducials)
  • Eugenio Marinetto, UC3M Madrid (Line-Based Registration, Reg. Algorithm)

Project Description

Objective

  • Add module for registration using linear features

Approach, Plan

  • Investigate module use cases:
    • Ultrasound calibration for PLUS
    • ...
  • Use Fiducial Registration Wizard module as a template
  • Investigate whether linear features can be treated in the same way as fiducials

Progress

  • See: LinearObjectRegistration
  • Discussed generic module for collecting/defining/acquiring parametric features:
    • Use data structure to store a set of 0D/1D/2D parametric features
    • For each feature, store raw point data, as well as basic information (i.e. centroid, variance, etc.)
    • Individual registration algorithms are implemented as modules using the data structure
    • Individual algorithm is responsible for converting 0D/1D/2D data to points/lines/planes (if applicable)
    • Methods for acquisition: tracking, models, fiducials, segmentation
  • Use cases:
    • Phantom registration for ultrasound calibration
    • Line feature based image to tracker registration
    • Fiducial frame registration
  • Began implementation of generic parametric features