Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake-city, Aichi 470-1192, Japan.
Int J Comput Assist Radiol Surg. 2013 Mar;8(2):193-205. doi: 10.1007/s11548-012-0767-5. Epub 2012 Jun 9.
Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations.
Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters.
The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80 % of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4-36 times faster than existing methods.
Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images.
现有的肺结节计算机辅助检测方案需要大量的计算，每个病例需要数十分钟；图像采集时间和结节检测时间之间存在较大差距。本研究提出了一种基于圆柱状结节增强滤波器的快速肺结节 CT 图像检测方案，旨在提高 CT 检查诊断的工作流程。
提出的检测方案涉及肺区的分割、预处理、结节增强、进一步分割和假阳性（FP）减少。作为结节增强，我们的方法采用圆柱形滤波器来减少计算量。使用支持向量机和七种特征参数来减少结节候选物中的假阳性（FP）。
使用 Lung Image Database Consortium 公开的图像数据库进行了实验评估，实验评估了检测性能和速度。五次交叉验证结果表明，我们的方法能够正确检测 80%的结节，每个病例的 FP 为 4.2，并且该方法的检测速度比现有方法快 4-36 倍。
检测性能和速度表明，我们的方法可能有助于快速检测 CT 图像中的肺结节。
