A Faster Second Opinion

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Comparing the entropy in images is a particularly attractive tactic, says Tourassi, because these values are automatically calculated for mammograms when they're submitted into the Duke database. Thus, no extra image-processing computation is needed for the technique.

In a pilot study, the Duke researchers showed that by comparing the image entropy of a suspicious region in a new mammogram to the entropy of all known cancerous regions in the database, they were able to slash the number of analyzed mammograms from about 2,300 to 600.

From there, says Tourassi, a more fine-grain analysis is used to compare the region in question with the known regions in database images. Since the system has to fully process only around 600 images, the computation effort is reduced by 75 percent, and the search can be done in seconds. Their results were presented this week at the annual meeting of the American Association of Physicists in Medicine in Orlando, FL.

In addition to speeding up the search process, the Duke technique could also improve the detection rate of cancerous lesions, says Maryellen Giger, professor of radiology at the University of Chicago. Image entropy searches, in particular, are well suited to detecting lesions. Current systems have an 80 percent accuracy rate in detecting this type of cancer indicator; Giger says the Duke technique could significantly improve that rate. "It's very promising," says Giger.

Within a year, the Duke researchers will launch a study to evaluate the clinical impact of their new technique.