基于规则的方法和人工神经网络对伴有间质性疾病的正常和异常肺部进行分类。
Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.
作者信息
Katsuragawa S, Doi K, MacMahon H, Monnier-Cholley L, Ishida T, Kobayashi T
机构信息
Department of Radiology, Iwate Medical University, Morioka, Japan.
出版信息
J Digit Imaging. 1997 Aug;10(3):108-14. doi: 10.1007/BF03168597.
We devised an automated classification scheme by using the rule-based method plus artificial neural networks (ANN) for distinction between normal and abnormal lungs with interstitial disease in digital chest radiographs. Four measures used in the classification scheme are determined from the texture and geometric-pattern feature analyses. The rms variation and the first moment of the power spectrum of lung patterns are determined as measures for the texture analysis. In addition, the total area of nodular opacities and the total length of linear opacities are determined as measures for the geometric-pattern feature analysis. In our classification scheme with these measures, we identify obviously normal and abnormal cases first by the rule-based method and then ANN is applied for the remaining difficult cases. The rule-based plus ANN method provided a sensitivity of 0.926 at the specificity of 0.900, which was considerably improved compared to performance of either the rule-based method alone or ANNs alone.
我们设计了一种自动分类方案，通过使用基于规则的方法加上人工神经网络（ANN），以区分数字化胸部X光片中正常肺部与患有间质性疾病的异常肺部。分类方案中使用的四项指标是通过纹理和几何图案特征分析确定的。肺纹理模式的均方根变化和功率谱的一阶矩被确定为纹理分析的指标。此外，结节状阴影的总面积和线状阴影的总长度被确定为几何图案特征分析的指标。在我们采用这些指标的分类方案中，首先通过基于规则的方法识别明显正常和异常的病例，然后将人工神经网络应用于其余难以判断的病例。基于规则加人工神经网络的方法在特异性为0.900时的灵敏度为0.926，与单独使用基于规则的方法或单独使用人工神经网络的性能相比有了显著提高。
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