An unusual new technique for pattern recognition and small object detection has successfully detected microcalcifications in digitized mammograms -- and holds promise for discerning other medical pathologies, manufacturing defects, and various objects in commercial, defense and Internet imagery.
Unlike most pattern recognition systems, this approach does not totally rely on the intervention of a human to extract and define a set of features for it. This new system is capable of efficiently searching a database of raw information to match patterns and detect objects.
"Instead of trying to extract a restricted set of features and train the classifier to look just for those features, we would have the expert user, in this case, the radiologist, directly build a database of known cancer indications encountered in the past," explained Dr. Christopher F. Barnes, a senior research engineer at the Georgia Tech Research Institute (GTRI). "We provide a quick-search software and firmware interface that allows this large database to be efficiently searched in near real time. Then, the radiologist's archive of past pathological cases essentially becomes a data classifier for processing new mammograms."
In its mammogram analysis, the software did not miss any microcalcifications in any of the digitized mammograms that were properly calibrated.
"In all of the data that's similar to our database data, this approach achieved nearly 100 percent detection with what appears to be acceptable levels of false alarms," Barnes said. "That's where you want to be with a mammogram -- we intend to provide a safety net for the expert-human analysis that may be susceptible to fatigue factors."
The system is not intended to replace radiologists or other medical
or manufacturing professionals, Barnes says. It will merely suggest
regions of mammograms or other data that should be given closer
attention, based on past observations. Furthermore, the system provides
Contact: John Toon
Georgia Institute of Technology Research News