Computerized detection of clustered microcalcifications in digital mammograms: Applications of artificial neural networks
作者:
Yuzheng Wu,
Kunio Doi,
Maryellen L. Giger,
Robert M. Nishikawa,
期刊:
Medical Physics
(WILEY Available online 1998)
卷期:
Volume 19,
issue 3
页码: 555-560
ISSN:0094-2405
年代: 1998
DOI:10.1118/1.596845
出版商: American Association of Physicists in Medicine
数据来源: WILEY
摘要:
Artificial neural networks have been applied to the differentiation of actual “true” clusters from normal parenchymal patterns and also to the differentiation of actual clusters from false‐positive clusters as reported by a computerized scheme for the detection of microcalcifications in digital mammograms. The differentiation was carried out in both the spatial and frequency domains. The performance of the neural networks was evaluated quantitatively by means of receiver operating characteristic (ROC) analysis. It was found that the networks could distinguish clustered microcalcifications from normal nonclustered areas in the frequency domain, and that they could eliminate approximately 50% of false‐positive clusters of microcalcifications while preserving 95% of the positive clusters, when applied to the results of the automated detection scheme. A large, comprehensive training database is needed for neural networks to perform reliably in clinical situations.
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