Improving surgery planning

MARCH/APRIL 2007 1541-1672/07/$25.00 © 2007 IEEE 5 Published by the IEEE Computer Society Improving diagnoses Getting a quicker, smarter look at medical images taken using computer-aided tomography (CAT scans) and magnetic resonance imaging would help doctors make faster, more accurate diagnoses. At Simon Fraser University, Ghassan Hamarneh and Chris McIntosh have developed virtual worms that can “crawl” through images of tubular structures such as blood vessels, airways, and spinal cords. These 3D crawlers analyze the image data using AI reasoning techniques and present their interpretation on a screen. Initially, Hamarneh analyzed medical images by inserting a deformable geometrical model into the image. This crawler deformed when it was attracted to certain structures in the image. But, says Harmeneh, the deformable models weren’t very reliable. So, he and his team extended the model by giving the crawler perception intelligence (the ability to interpret or identify structures that are sensed) and decision-making capabilities. A “vesselness filter” helps the improved crawler know when it’s in a tubular structure. The filter collects image intensity data and uses it to determine whether the brightness level indicates a tubular structure—something to crawl down, such as a blood vessel. The crawler’s feature-detecting algorithms are based on a Hessian matrix that formally describes how the intensity changes. The algorithms obtain both eigenvalues and the corresponding eigenvectors. “The nice thing is the smallest eigenvector of this matrix points along tubular structures,” Hamarneh says. To detect branches in the tubes, the crawler has a spherical sensor that also collects image intensity data. The presence of multiple areas with a distinct brightness intensity, highlighted using image-processing algorithms, could indicate branching. Decision-making algorithms help the crawler decide where to crawl and when to stop. The crawler decides to follow a branch when it detects a bifurcation and stops when the tube narrows to the point that the tubular structure is ending. Hamarneh and his team are working on making the GUI more user friendly and making the crawler faster by porting their prototype system, which uses the MATLAB programming language, to ITK (the Insight Segmentation and Registration Toolkit), which uses C++. Next, Hamarneh says, they will tackle the more complex problem of interaction between multiple crawlers.