Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.
Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.
复制标题
数字乳房X光照片中簇状微钙化的计算机化检测：人工神经网络的应用。
DOI：
10.1118/1.596845
复制
发表时间：
1992
期刊：
影响因子：
3.8
通讯作者：
Nishikawa,RM
中科院分区：
文献类型：
--
作者：
Wu,Y;Doi,K;Giger,ML;Nishikawa,RM
人工神经网络已经被应用于从正常的实质图案中区分实际的“真”簇，并且还被应用于区分实际簇和假阳性簇，如用于检测数字乳房X光照片中的微钙化的计算机化方案所报告的那样。分别在空间域和频率域进行了区分。通过接收器工作特性(ROC)分析对神经网络的性能进行了定量评估。结果发现，当应用于自动检测方案的结果时，网络可以在频域中区分聚集的微钙化和正常的非聚集区域，并且它们可以消除大约50%的微钙化的假阳性簇，同时保留95%的阳性簇。为了让神经网络在临床情况下可靠地运行，需要一个大型的、全面的训练数据库。
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.