Knowledge-based computer-aided detection of masses on digitized mammograms: a preliminary assessment.
The purpose of this work was to develop and evaluate a computer-aided detection (CAD) scheme for the improvement of mass identification on digitized mammograms using a knowledge-based approach. Three hundred pathologically verified masses and 300 negative, but suspicious, regions, as initially identified by a rule-based CAD scheme, were randomly selected from a large clinical database for development purposes. In addition, 500 different positive and 500 negative regions were used to test the scheme. This suspicious region pruning scheme includes a learning process to establish a knowledge base that is then used to determine whether a previously identified suspicious region is likely to depict a true mass. This is accomplished by quantitatively characterizing the set of known masses, measuring "similarity" between a suspicious region and a "known" mass, then deriving a composite "likelihood" measure based on all "known" masses to determine the state of the suspicious region. To assess the performance of this method, receiver-operating characteristic (ROC) analyses were employed. Using a leave-one-out validation method with the development set of 600 regions, the knowledge-based CAD scheme achieved an area under the ROC curve of 0.83. Fifty-one percent of the previously identified false-positive regions were eliminated, while maintaining 90% sensitivity. During testing of the 1,000 independent regions, an area under the ROC curve as high as 0.80 was achieved. Knowledge-based approaches can yield a significant reduction in false-positive detections while maintaining reasonable sensitivity. This approach has the potential of improving the performance of other rule-based CAD schemes.
Chang
Y H
YH
Department of Radiology, University of Pittsburgh, Pennsylvania 15261-0001, USA.
Hardesty
L A
LA
Hakim
C M
CM
Chang
T S
TS
Zheng
B
B
Good
W F
WF
Gur
D
D
D001943:Breast Neoplasms / Q000175:diagnosis; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008327:Mammography / Q000295:instrumentation / Q000379:methods*; D015233:Models, Statistical; D012372:ROC Curve; D012984:Software