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Much of science is defining appropriate models to explain experimental results. Since each measurement contains some artifacts of the tool used to make it and each display of data biases the user towards a particular interpretation, scientists are constantly aware that preliminary interpretations must be validated against the actual data. The goal of this work is to combine techniques for acquisition, analysis, model building, hypothesis testing, and direct visual comparison into an experiment-driven system than enables the scientist to separate truth from illusion.

Image Analysis: Estimated models from images

The image above on the right shows a model of a tube that was extracted from an AFM image using MIDAG-produced CORE-tracking software. The model is drawn in green raised above the image from which it was extracted. This software is able to extract the 3D shape of the tube along with its width and curvature information at each point along the tube. This software enables us to extract and analyze tubular objects such as DNA, fibrin, and mucin from images. We have integrated the CORE-tracking code, originally developed for tracing blood vessels and other structures in medical images, into our microscopy applications.

The image on the left shows a trace of DNA on an AFM image as it passes through a protein. The boundary of the DNA as detected by the Resource code is shown as a thin blue line. The medial axis of the DNA is shown as a thin red line. The interpolated trace of the DNA and an estimated bend angle is indicated by a thin green line passing through the protein. The use of this code produced an order-of-magnitude decrease in analysis time and more accuracy than the manual estimation being routinely used before.

By feeding these extracted models to the AFM simulator (described next), a scientist can investigate which portions of the AFM scans are well-explained by the model. Using direct visual comparison between the model and scan (described below), a scientist can compare the model's fit and discover where it needs improving.

If you are interested in using our Tube Tracer model-from-image code, visit our software download page.

Imaging Simulator:
Estimated images from models

As described in our SPIE paper, hardware acceleration enable interactive calculation of imaging artifacts that would be expected from scanning a model with an atomic force microscope (AFM). This enables direct comparison between experimental results and expected microscope scans for both hand-made models and atom-coordinate data. The images to the right show this technique applied to a DNA/lac-repressor complex. The four images in the upper right corner show the result of imaging the crystallographics atom coordinates with increasingly coarse AFM tips. The bottom image shows a simplified model of a DNA strand wrapped around a protein; the image above it shows the AFM scan expected when this model is scanned. The image in the upper left shows an actual AFM scan of DNA wrapped around a protein. Such comparisons have enabled Dorothy Erie's chemistry group to determine which of several possible wrappings occur in actual experiments by showing that conformations which were not seen experimentally would have been resolvable with AFM had they occured, ruling out their being hidden by imaging or reconstruction artifacts.

We are working on making our AFM-image-of-model code available for outside use. If you are interested in using this code when it becomes available (hopefully Fall of 2002), email Russell Taylor to get on our distribution list

New Visualizations: Direct comparison of model and experiment

We are developing visualization techniques to enable the direct visual comparison of model and experiment. The image sequence above shows three techniques for the simultaneous display of an AFM scan of an adenovirus and an icosahedral model of the virus to determine the orientation of the virus that was scanned. The left image shows standard transperency, which has been shown to be not useful because it destroys the user's perception of the transparent surface. The center image uses a subsampled wire-frame view of the surface; it clearly shows both images but suffers from aliasing to the underlying scan mesh and from imprecise registration to the scan surface. The image to the right shows a partially-transparent texture applied to the surface; when combined with a 3D view of the surface and user-controlled viewpoint, this technique enables effective comparison between the two surfaces. We are continuing to investigate new techniques for such display and to validate them with user studies.

This work is also being applied to the visualization of uncertain surfaces (surfaces extracted from volume data where the gradient was low, multiple segmentations of a tumor surface by several radiologists, etc.).