Arzhaeva Yulia, Prokop Mathias, Tax David M J, De Jong Pim A, Schaefer-Prokop Cornelia M, van Ginneken Bram
Images Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Med Phys. 2007 Dec;34(12):4798-809. doi: 10.1118/1.2795672.
A computer-aided detection (CAD) system is presented for the localization of interstitial lesions in chest radiographs. The system analyzes the complete lung fields using a two-class supervised pattern classification approach to distinguish between normal texture and texture affected by interstitial lung disease. Analysis is done pixel-wise and produces a probability map for an image where each pixel in the lung fields is assigned a probability of being abnormal. Interstitial lesions are often subtle and ill defined on x-rays and hence difficult to detect, even for expert radiologists. Therefore a new, semiautomatic method is proposed for setting a reference standard for training and evaluating the CAD system. The proposed method employs the fact that interstitial lesions are more distinct on a computed tomography (CT) scan than on a radiograph. Lesion outlines, manually drawn on coronal slices of a CT scan of the same patient, are automatically transformed to corresponding outlines on the chest x-ray, using manually indicated correspondences for a small set of anatomical landmarks. For the texture analysis, local structures are described by means of the multiscale Gaussian filter bank. The system performance is evaluated with ROC analysis on a database of digital chest radiographs containing 44 abnormal and 8 normal cases. The best performance is achieved for the linear discriminant and support vector machine classifiers, with an area under the ROC curve (A(z)) of 0.78. Separate ROC curves are built for classification of abnormalities of different degrees of subtlety versus normal class. Here the best performance in terms of A(z) is 0.90 for differentiation between obviously abnormal and normal pixels. The system is compared with two human observers, an expert chest radiologist and a chest radiologist in training, on evaluation of regions. Each lung field is divided in four regions, and the reference standard and the probability maps are converted into region scores. The system performance does not significantly differ from that of the observers, when the perihilar regions are excluded from evaluation, and reaches A(z) = 0.85 for the system, with A(z) = 0.88 for both observers.
本文介绍了一种用于胸部X光片中间质性病变定位的计算机辅助检测（CAD）系统。该系统采用两类监督模式分类方法对整个肺野进行分析，以区分正常纹理和受间质性肺病影响的纹理。分析是逐像素进行的，并生成一个图像概率图，其中肺野中的每个像素都被赋予一个异常概率。间质性病变在X光片上通常很细微且边界不清，即使对于专业放射科医生来说也很难检测到。因此，本文提出了一种新的半自动方法来为训练和评估CAD系统设定参考标准。该方法利用了这样一个事实，即间质性病变在计算机断层扫描（CT）上比在X光片上更清晰。在同一患者的CT扫描冠状切片上手动绘制的病变轮廓，利用一小部分解剖标志点的手动指示对应关系，自动转换为胸部X光片上的相应轮廓。对于纹理分析，使用多尺度高斯滤波器组来描述局部结构。在一个包含44例异常病例和8例正常病例的数字胸部X光片数据库上，通过ROC分析对系统性能进行评估。线性判别和支持向量机分类器的性能最佳，ROC曲线下面积（A(z)）为0.78。针对不同细微程度的异常与正常类别分类，构建了单独的ROC曲线。在此，对于明显异常像素与正常像素的区分，A(z)方面的最佳性能为0.90。在区域评估方面，将该系统与两名人类观察者（一位专业胸部放射科医生和一位正在接受培训的胸部放射科医生）进行了比较。每个肺野被划分为四个区域，参考标准和概率图被转换为区域分数。当在评估中排除肺门周围区域时，系统性能与观察者的性能没有显著差异，系统的A(z) = 0.85，两名观察者的A(z) = 0.88。
