Improved mammography

Researchers have found that digitised mammograms are interpreted more accurately once they have been “compressed” using techniques similar to those used to lessen memory demands of images in digital cameras.

A team of researchers has found that digitised mammograms, the X-ray cross sections of breast tissue that doctors use to search for cancer, are actually interpreted more accurately by radiologists once they have been “compressed” using techniques similar to those used to lessen the memory demand of images in digital cameras.

Though compression strips away much of the original data, it still leaves intact those features that physicians need most to diagnose cancer effectively. Perhaps equally important, digitisation could bring mammography to many outlying communities via mobile equipment and dial-up Internet connections.

“Any technique that improves the performance of radiologists is helpful, but this also means that mammograms can be taken in remote places that are under served by the medical community,” said Bradley J. Lucier, who developed the file-compression method and is a professor of mathematics and computer science at Purdue University, West Lafayette. “The mammograms can then be sent electronically to radiologists, who can read the digitised versions knowing they will do at least as well as the original mammograms.”

A research paper appears in today’s issue of Radiology, the journal of the Radiological Society of North America. Lucier’s file-compression method was run at the Moffitt Cancer Center at the University of South Florida in Tampa.

Discerning the potential seeds of cancer within the chaff of extraneous detail present in a mammogram requires the expert eye of a radiologist, who must pick out salient features at many different scales within the image. Clues can be very small clusters of tiny calcium deposits, each less than one-hundredth of an inch in diameter. Clues also can range up through the edges of medium-sized objects – which could be benign cysts with smooth edges, for example, or cancerous tumours with rough edges – up to large-scale patterns in tissue fibre.

“The edges of tumours are where growth occurs, and they tell radiologists whether what they see is a tumour or not,” Lucier said. “You have to keep all these features intact when you compress the image if it is to be useful.”

Once a mammogram image has been converted into electronic form, it can contain more than 50 megabytes of data, which makes it prohibitively large for transmission by computer modem over a telephone line. Compounding the issue is that four such images are needed for a complete screening, and though it takes only a few minutes to obtain the X-ray pictures, getting a mammogram can be difficult. A 2001 US Food and Drug Administration (FDA) study showed that the number of mammography facilities has declined in most states, and the population of potential recipients of mammography services has increased. While the study suggests that difficulties obtaining mammograms are localised rather than widespread, Lucier said that telemedicine could potentially mitigate the problem.

“I began experimenting with file-compression algorithms to see if we could shrink files to the point where they could be sent over standard phone lines,” he said. “Some communities do not have easy access to broadband Internet yet, and my colleagues and I wanted to work around that issue.”

Lucier found that one well-tested algorithm – a short set of instructions that can be repeated many times – did the trick after a bit of tweaking. Though the basic mathematics has been around for more than a decade, he said, its finer points required some adjusting.

“I wanted the algorithm to make all the features important to radiologists degrade at the same rate – both the edges of large tumours and the smallest calcium deposits,” Lucier said. “I tried several approaches and eventually got a balance that seemed reasonable, based on what radiologists tell me they want.”

On seven of nine measures of diagnostic accuracy, radiologists interpret the compressed images more accurately than they interpret the original images, even though the compressed images contain, on average, only two percent of the information in the originals.

“I want to emphasise that this study does not necessarily imply that compression always improves diagnosis,” Lucier said. “It means that radiologists can spot and localise features as well or better than before. The technology filters out the noise, if you will. But so far, there is no question that these radiologists did diagnose better using the compressed images.”

Lucier is optimistic that the technique might be applied to other forms of telemedicine as well, if certain modifications are made.

“There are many forms of medical diagnosis that require an image to be read by a specialist,” he said. “If image compression is applied to other diagnostic situations, you won’t actually have to have that specialist on hand if you can get the equipment to the patient. But this is proof in principle that file compression, if done properly, can confer advantages to both patient and doctor.”