Department of Medical Radiological Technology, Kagoshima Medical Technology College, 5417-1 Hirakawa, Kagoshima, 891-0133, Japan.
J Digit Imaging. 2011 Dec;24(6):1126-32. doi: 10.1007/s10278-010-9357-7.
It is difficult for radiologists to classify pneumoconiosis with small nodules on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on the rule-based plus artificial neural network (ANN) method for distinction between normal and abnormal regions of interest (ROIs) selected from chest radiographs with and without pneumoconiosis. The image database consists of 11 normal and 12 abnormal chest radiographs. These abnormal cases included five silicoses, four asbestoses, and three other pneumoconioses. ROIs (matrix size, 32 × 32) were selected from normal and abnormal lungs. We obtained power spectra (PS) by Fourier transform for the frequency analysis. A rule-based method using PS values at 0.179 and 0.357 cycles per millimeter, corresponding to the spatial frequencies of nodular patterns, were employed for identification of obviously normal or obviously abnormal ROIs. Then, ANN was applied for classification of the remaining normal and abnormal ROIs, which were not classified as obviously abnormal or normal by the rule-based method. The classification performance was evaluated by the area under the receiver operating characteristic curve (Az value). The Az value was 0.972 ± 0.012 for the rule-based plus ANN method, which was larger than that of 0.961 ± 0.016 for the ANN method alone (P ≤ 0.15) and that of 0.873 for the rule-based method alone. We have developed a rule-based plus pattern recognition technique based on the ANN for classification of pneumoconiosis on chest radiography. Our CAD system based on PS would be useful to assist radiologists in the classification of pneumoconiosis.
对于放射科医生来说，在胸部 X 光片上对小结节性尘肺进行分类具有一定难度。因此，我们开发了一种基于规则加人工神经网络（ANN）方法的计算机辅助诊断（CAD）系统，用于区分有和无尘肺的胸部 X 光片中正常和异常感兴趣区域（ROI）。该图像数据库包含 11 张正常和 12 张异常胸部 X 光片。这些异常病例包括 5 例矽肺、4 例石棉肺和 3 例其他尘肺。ROI（矩阵大小，32×32）是从正常和异常肺中选择的。我们通过傅里叶变换获得了频谱（PS），用于频率分析。使用 PS 值为 0.179 和 0.357 周/毫米（对应于结节模式的空间频率）的规则方法用于识别明显正常或异常的 ROI。然后，将 ANN 应用于规则方法未分类为明显正常或异常的剩余正常和异常 ROI 的分类。通过受试者工作特征曲线下的面积（Az 值）评估分类性能。基于规则加 ANN 方法的 Az 值为 0.972±0.012，大于单独使用 ANN 方法的 0.961±0.016（P≤0.15）和单独使用规则方法的 0.873。我们已经开发了一种基于规则加基于 ANN 的模式识别技术，用于对胸部 X 光片上的尘肺进行分类。我们基于 PS 的 CAD 系统将有助于放射科医生对尘肺进行分类。
