Automated lung nodule classification following automated nodule detection on CT: A serial approach
Automated lung nodule classification following automated nodule detection on CT: A serial approach
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DOI：
10.1118/1.1573210
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发表时间：
2003-06-01
期刊：
影响因子：
3.8
通讯作者：
Roy, AS
中科院分区：
文献类型：
--
作者：
Armato, SG;Altman, MB;Roy, AS
我们评估了自动分类器在低剂量螺旋CT扫描中用于区分良恶性肺结节的性能，该扫描是肺癌筛查计划的一部分。以这种方式分类的结节最初由我们的自动肺结节检测方法识别，从而将自动肺结节检测的输出作为自动肺结节分类的输入。这项研究开始缩小“检测任务”和“分类任务”之间的区别。肺结节的自动检测是基于对CT图像数据的二维和三维分析。采用灰度阈值技术识别初始候选肺结节，计算其形态特征和灰度特征。采用基于规则的方法减少与非结节相对应的候选结节数，并通过线性判别分析融合剩余候选结节的特征，得到最终的检测结果。自动肺结节分类通过另一个线性判别分类器融合检测算法识别出的与实际结节对应的候选肺结节的特征，以区分结节的良恶性。自动分类方法被应用于从393张低剂量胸部CT扫描的数据库中获得的计算机检测结果，该数据库包含470个确认的肺结节(69个恶性结节和401个良性结节)。使用接收器操作特征(ROC)分析来评估分类器区分与恶性结节对应的候选结节和与良性病变对应的候选结节的能力。这一分类任务的ROC曲线下面积在留一法评估中达到了0.79。(C)2003年美国医学物理学家协会。
We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the "detection task" and the "classification task." Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation. (C) 2003 American Association of Physicists in Medicine.