Dept. of Radiol., Georgetown Univ. Med. Centre, Washington, DC.
IEEE Trans Med Imaging. 1995;14(4):711-8. doi: 10.1109/42.476112.
We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.
我们开发了一种双匹配方法和人工视觉神经网络技术，用于肺结节检测。该神经网络技术通常适用于灰度成像中医学图像模式的识别。人工神经网络的结构是简化的人类视觉网络结构。人工神经网络的基本操作是局部二维卷积，而不是加权乘法的全连接。卷积核的权值系数是由神经网络通过反向传播训练形成的。此外，我们模拟了放射科医生的阅读过程，以便指导人工神经网络识别预先定义的图像模式和放射科专家感兴趣的图像模式。我们已经测试了这种用于肺结节检测的方法。性能研究表明，该技术在临床环境中有潜在的应用。该程序首先使用球形模板双匹配技术，以高灵敏度进行初始结节搜索，检测圆形物体。人工卷积神经网络作为最终分类器，用于确定可疑图像块是否包含肺结节。使用预扫描和卷积神经网络评估进行自动检测肺结节的总处理时间约为 15 秒，在 DEC Alpha 工作站上。
