Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

Published

Conference Paper

© 1993 SPIE. All rights reserved. We are developing an "intelligent" workstation to assist radiologists in diagnosing breast cancer from mammograms. The hardware for the workstation will consist of a film digitizer, a high speed computer, a large volume storage device, a film printer, and 4 high resolution CRT monitors. The software for the workstation is a comprehensive package of automated detection and classification schemes. Two rule-based detection schemes have been developed, one for breast masses and the other for clustered microcalcifications. The sensitivity of both schemes is 85% with a false-positive rate of approximately 3.0 and 1.5 false detections per image, for the mass and cluster detection schemes, respectively. Computerized classification is performed by an artificial neural network (ANN). The ANN has a sensitivity of 100% with a specificity of 60%. Currently, the ANN, which is a three-layer, feed-forward network, requires as input ratings of 14 different radiographic features of the mammogram that were determined subjectively by a radiologist. We are in the process of developing automated techniques to objectively determine these 14 features. The workstation will be placed in the clinical reading area of the radiology department in the near future, where controlled clinical tests will be performed to measure its efficacy.

Full Text

Duke Authors

Cited Authors

  • Nishikawa, RM; Giger, ML; Doi, K; Vyborny, CJ; Schmidt, RA; Metz, CE; Wu, Y; Yin, FF; Jiang, Y; Huo, Z; Lu, P; Zhang, W; Ema, T; Bick, U; Papaioannou, J; Nagel, RH

Published Date

  • July 29, 1993

Published In

Volume / Issue

  • 1905 /

Start / End Page

  • 422 - 432

Electronic International Standard Serial Number (EISSN)

  • 1996-756X

International Standard Serial Number (ISSN)

  • 0277-786X

Digital Object Identifier (DOI)

  • 10.1117/12.148655

Citation Source

  • Scopus