Dear Advanced Visualization Insider,
While computer-aided detection (CAD) technology is an invaluable tool, the high number of false positives can be daunting for radiologists. At the recent Computer Assisted Radiology and Surgery (CARS) conference in Japan, researchers from Canada discussed a lung CAD algorithm that may help.
The sophisticated algorithm can distinguish between faint and similarly shaped structures, reducing false positives by means of a hybrid learning scheme that uses fuzzy logic and c-means clustering, according to Javad Alirezaie, Ph.D., from Ryerson University in Toronto. Staff writer Eric Barnes reports on the group's research.
As an Advanced Visualization Insider subscriber, you have access to this Insider Exclusive article before it is published for the rest of our AuntMinnie members. To learn more about reducing false positives in lung CAD, click here.
Also, be sure to check out our other coverage from CARS, including articles on the growth in medical image processing technology, the future of medical imaging, the role of CAD in emphysema detection, breast CAD developments, and the growth of MR-guided intervention procedures in Japan.
Is there a topic you'd like to see covered, or are you interested in submitting an article to AuntMinnie.com? Please feel free to drop me a line.
