Visualizing Image Model Statistics for the Human Kidney Liz Dolan, Joshua Stough COMP December 2, 2003

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

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) 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 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 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 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 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 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?