TubeTK/Events/2010.07.26

Andinet

• Primary goal: Data from Duke for BWH
• Accomplishments
  • Attend NAMIC AHM
  • Determine what is necessary to record data sent to OpenIGTLink from VectorVision system
    • Define data workflow and software architecture
    • Begin implementation
    • Product: powerpoint presentation: 5 slides
• Near term (August 1)
  • Install VV at Duke
  • Determine if we can get US data from Duke machine
  • Write IJ article
    • Cite grant proposal in article
• Medium term (1.5 months, August 15)
  • Investigate simulation of ultrasound from MR/CT (Talk to Stephen First :) )
  • Code review of vessel segmentation method from Stephen

Patrick

• Primary goal: Bump and dent identification on IC images
• Accomplishments
  • Explore new features
    • z-score values from three different mean/stdDev joint histograms: add, subtract, and unchanged
    • evaluate a variety of standard deviations for intensity and ridge computations
  • New centerline method (skeletonization)
  • GenerateFeaturesForWeka
• Near Term (Aug 1)
  • Get registered data from Greg
  • compute dot-product between line (hessian) tangent and normal directions in ES and GDS images
  • write program that goes from Weka output to image and computes TPR/FPR scores on that image
  • Collaborate with Casey
    • Choose classification scheme
    • Implement in C++ or python - in tubetk
      • Use neuralnets / parzenWindowing in ITK
    • Subselect features
  • Product: ~ 5 slides / report to USC illustrating path chosen, strengths, and weaknesses.
    • Real-world tests / workflow
    • Does a trained classifier work on other layers?
    • Does a trained classifier work on other acquisitions?
      • i.e., do we need to insert modifications for training on every slice / acquisition / ?
      • Normalizing for inter-acquisition (or inter-slice) variations?
  • Go to Synchrotron
• Medium term (1 months, August 15)
  • Delivery and education
  • Can we get better in simulation?
  • Connectivity analysis

Casey

• Primary goal: Compare populations of vascular networks
• Near term (0.5, Aug 1)
  • Collaborate with Patrick
    • Feature extraction
      • Patch-based features (max, median, quantiles)
      • Scales / neighborhood
    • Feature research
      • Width estimate from Stephen (concern, time)
      • Location of local max in ridgeness
      • Subsample centerlines
      • Optimal match filter
    • Classifiers
      • Comparison
      • Implementation for transfer to USC
      • Hierarchy (Good/bad. If bad, then add/sub.)
  • Data for Retinas
• Medium term (1 months, Aug 15)
  • Review previous processing pipeline with Stephen
  • Research on methods for comparing spatial graphs / adjacency matrices
  • Begin Port and test existing adjacency code
  • Process retinal data
  • Complete port and test of existing adjacency code
    • Prepare IJ article

Hua

• Primary goal: ultrasound image processing
• Accomplished
  • Verify Andinet's code: add tests and help with IJ publication
    • Creating tests (sin pattern with known derivatives)
• Near term (August 1)
  • Increase coverage of TubeTK
  • Update registration code
  • Begin investigation of registration metrics that depend on ultrasound probe orientation
    • Get data from InnerOptic
      • Design phantom or use something from InnerOptic
      • Discuss with InnerOptic
      • CIRS Phantom
      • http://www.insight-journal.org/midas/item/view/2206
      • http://www.insight-journal.org/midas/item/view/117
• Medium term (1.5 months
  • Investigate use of speckle in ultrasound registration
  • Model-based deformation field interpolation
  • 2D-3D registration (data)
  • Simulating ultrasound from MR/CT