Sluimer Ingrid C, van Waes Paul F, Viergever Max A, van Ginneken Bram
Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Med Phys. 2003 Dec;30(12):3081-90. doi: 10.1118/1.1624771.
A computer-aided diagnosis (CAD) system is presented to automatically distinguish normal from abnormal tissue in high-resolution CT chest scans acquired during daily clinical practice. From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi-scale filter bank. The use of various classifiers and feature subsets is compared and results are evaluated with ROC analysis. Performance of the system is shown to approach that of two expert radiologists in diagnosing the local regions of interest, with an area under the ROC curve of 0.862 for the CAD scheme versus 0.877 and 0.893 for the radiologists.
本文提出了一种计算机辅助诊断（CAD）系统，用于在日常临床实践中获取的高分辨率胸部CT扫描中自动区分正常组织和异常组织。从116名患者的高分辨率计算机断层扫描中提取了657个感兴趣区域，这些区域将被分类为显示正常或异常肺组织。采用了一种有原则的纹理分析方法，通过多尺度滤波器组提取描述局部图像结构的特征。比较了各种分类器和特征子集的使用情况，并通过ROC分析评估结果。结果表明，该系统在诊断感兴趣局部区域方面的性能接近两位放射科专家，CAD方案的ROC曲线下面积为0.862，而放射科专家分别为0.877和0.893。
