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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. 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

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  1. Visualizing Image Model Statistics for the Human Kidney Liz Dolan, Joshua Stough COMP 290-069 December 2, 2003

  2. Overview • Goal: Evaluate image models. • Data Description • Design • Implementation • Conclusions, Audience Feedback

  3. 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

  4. 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)?

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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?

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