Development of an improved CAD scheme for automated detection of lung nodules in digital chest images
Development of an improved CAD scheme for automated detection of lung nodules in digital chest images
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DOI：
10.1118/1.598028
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发表时间：
1997-09-01
期刊：
影响因子：
3.8
通讯作者：
Giger, ML
中科院分区：
文献类型：
--
作者：
Xu, XW;Doi, K;Giger, ML
肺癌是美国男性和女性癌症死亡的主要原因，5年生存率仅约13%。然而，如果在早期诊断和治疗，这种存活率可以提高到47%。在这项研究中，我们开发了一种改进的计算机辅助诊断（CAD）方案，用于自动检测数字胸部图像中的肺结节，以帮助放射科医生，他们在日常实践中可能会错过高达30%的实际阳性病例。200个PA胸片，100个正常和100个异常，被用作我们研究的数据库。在100例异常病例中，结节的存在由两名经验丰富的放射科医生根据CT扫描或放射学随访证实。在我们的CAD方案中，结节候选者最初通过差分图像的多个灰度阈值化（其对应于信号增强图像和信号抑制图像的减法）来选择，然后被分类为六组。通过基于规则的自适应测试和人工神经网络（ANN）消除了大量误报。CAD方案平均达到70%的灵敏度，每个胸部图像有1.7个假阳性，与其他研究相比，该性能明显更好。在IBM RISC/6000 Powerstation 590上，处理一个胸部图像的CPU时间约为20秒。我们认为，CAD方案与目前的性能是准备进行初步的临床评价。(C)1997年美国医学物理学家协会。
Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital chest images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference image (which corresponds to the subtraction of a signal-enhanced image and a signal suppressed image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per chest image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one chest image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation. (C) 1997 American Association of Physicists in Medicine.