Interesting and counter-intuitive news is being reported by researchers from Purdue University. The research, that originally focused on making mammography files compressed for faster transmission, revealed that smaller and more coarse pictures tend to augment abnormalities, and hence tend to be more sensitive for diagnostic purposes:
The research paper will appear in today’s (Dec. 20) issue of Radiology, the journal of the Radiological Society of North America. Lucier [Bradley J. Lucier, from the department of math at Purdue -ed.] developed the file-compression method used in the study, which 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 tumors with rough edges – up to large-scale patterns in tissue fiber.
“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 tumors 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.”
His methods have evidently paid off: 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 2 percent of the information in the originals.
“I want to emphasize that this study does not necessarily imply that compression always improves diagnosis,” Lucier said. “It means that radiologists can spot and localize 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.”
Any ideas out there what the “algorithm based on scale-specific quantization of biorthogonal wavelet coefficients” is all about? We, simple clinicians, are looking for an explanation!
The press release and abstract are here…