公开(公告)号：US5873824A

公开(公告)日：1999-02-23

申请号：US758438

申请日：1996-11-29

申请人： Kunio Doi ,  Takayuki Ishida ,  Shigehiki Katsuragwa

发明人： Kunio Doi ,  Takayuki Ishida ,  Shigehiki Katsuragwa

IPC分类号： A61B5/00 ,  G06F15/18 ,  G06N3/00 ,  G06Q50/00 ,  G06T1/00 ,  G06T7/00

CPC分类号： G06K9/00 ,  G06T7/0012 ,  G06K2209/05 ,  Y10S128/925

摘要： An automated computer-aided diagnosis (CAD) method and system using artificial neural networks (ANNs) for the quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized two-dimensional chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs. The second ANN was trained using vertical output patterns obtained from the 1.sup.st ANN for each ROI. The output value of the 2.sup.nd ANN was used to distinguish between normal and abnormal ROIS with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a certain threshold level, the chest image was considered abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images where the chest image was not clearly normal or abnormal. The ANN trained with image data learns some statistical properties associated with interstitial infiltrates in chest radiographs. In addition, the same technique can be applied to higher-dimensional data (e.g., three-dimensional data and four-dimensional data including time-varying three-dimensional data).

摘要翻译： 一种使用人工神经网络（ANN）进行图像数据定量分析的自动化计算机辅助诊断（CAD）方法和系统。 应用三个独立的ANN来检测数字化二维胸部图像上的间质性疾病。 在正常和异常的胸片中选择感兴趣区域（ROI）中的第一个ANN进行了水平剖面的训练。 使用从每个投资回报率的第一ANN获得的垂直输出模式训练第二ANN。 第二ANN的输出值用于区分正常和异常的ROIS与间质浸润。 如果胸部图像中异常ROI的数量与所有ROI的总数之比大于某一阈值水平，则胸部图像被认为是异常的。 另外，第三个ANN应用于区分胸部图像不正常或异常的正常和异常胸部图像。 用图像数据训练的ANN学习了与胸部X光片中的间质浸润相关的一些统计特性。 此外，相同的技术可以应用于高维数据（例如，三维数据和包括时变三维数据的四维数据）。