A Faster Second Opinion

A method for searching mammogram databases that uses image entropy could lead to faster and more accurate detection of breast cancer.

Software that compares a patient's mammogram to those in a database has become a valuable tool to help physicians more reliably detect breast cancer. However, as the size of these databases grows, the time it takes to evaluate a new image increases.

Image on left is a mammogram with a region selected where a cancerous lesion might exist. A computer compares it to images of known cancerous lesions in a database. (Credit: Georgia Tourassi, Duke University)

Now researchers from Duke University have developed a method for sorting through thousands of mammogram images, and picking out the most informative ones, in a matter of seconds.

Spotting a problem in mammograms can be difficult. For one thing, cancerous lesions can blend into the background, making them difficult for doctors to pick out. Software-based detection systems assist by comparing a new mammogram to a database of already analyzed ones, flagging cancer indicators that a physician might have missed.

The first such software-based system was approved by the Food and Drug Administration in 1998. Currently, more than 1,600 such systems are used in clinical practice in the United States. While the systems have been shown to increase the rate of breast cancer detection, they're still far from perfect, and researchers are constantly trying to improve their accuracy, says Georgia Tourassi, professor of radiology at Duke University and lead developer of the new software.

Newer "knowledge-based" software programs, such as the ones used by the Duke researchers, allow doctors to interact with the system -- asking questions and getting answers based on previous cancer cases in the database. However, churning through all the images in a knowledge-based system, which may contain thousands of mammograms, is time consuming. "As the knowledge database increases in size, this brute force method doesn't make sense anymore," says Tourassi.

To speed up the image-searching process, Tourassi and her team divided it into two steps. First they looked for the most useful mammograms in the database by using "image entropy" -- the amount of grayscale variation in the pixels -- on suspected mammographic regions. An all-black or all-white image has zero entropy, while more complex images have higher degrees of entropy, produced by patches of pixels with varying intensity. These high-entropy cases occur around images of cancerous lesions and are the most useful in evaluating new mammograms.