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Visualizing Image Model Statistics for the Human Kidney. Liz Dolan, Joshua Stough COMP 290-069 December 2, 2003. Overview. Goal: Evaluate image models. Data Description Design Implementation Conclusions, Audience Feedback. Segmentation of Kidneys in CT Scans.

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visualizing image model statistics for the human kidney

Visualizing Image Model Statistics for the Human Kidney

Liz Dolan, Joshua Stough

COMP 290-069

December 2, 2003

overview
Overview
  • Goal: Evaluate image models.
  • Data Description
  • Design
  • Implementation
  • Conclusions, Audience Feedback
segmentation of kidneys in ct scans
Segmentation of Kidneys in CT Scans
  • Deformable Model Segmentation
    • Geometric Typicality, Image Match (Bayesian)
  • CT Scans: Brightness  Density
  • Image Data Format, Profiles: cross-boundary normal sampling of image intensity.
  • Multiple Cases with correspondence
a clustering image model example
A Clustering Image Model (example)
  • Idea: Neighbor organs may be distant, near or adjacent (light-dark, notch, dark-light).
  • Each point responds to each template. Which template is popular where (choice per point)?
goals
Goals
  • Drive the image model evaluation.
  • View the observed data’s consistency with (response to) an image model, with respect to kidney anatomy (intuitiveness).
  • Locate differences in the image data response between models
the data
The Data
  • Kidney Boundary: 2D surface in 3D.
    • 2562 points on kidney
  • Irregular Grid, point sampled, no missing values.
  • Response is ratio scalar on the surface.
  • Certain models require nominal field for description.
  • Floats, numerical issues do not affect display.
the design
The Design
  • 3D shape vs. split: local model response.
  • Contours: to display ratio data.
  • Pseudocolor for ratio: for context, reinforcement, annotation.
    • Smooth, to show actual data differences
  • Pseudocolor for nominal: describe image model.
  • Multiple displays: compare models and provide context.
  • Interactive motion.
implementation
Implementation
  • Synchronized views of common model.
  • Each view of a different data set.
  • VTK, Python/Tk GUI.
    • Compare to AVS: more control, efficient user interface for loading datasets.
to be completed
To be Completed
  • Labeling the views by filename.
  • Texture for the nominal field, on the same view as ratio field, if not too high frequency.
  • Contour values.
  • Maybe labels for nominal field.
  • Audience Suggestions?