Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 123 Cheomdan-gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea(1).
Comput Methods Programs Biomed. 2014;113(1):37-54. doi: 10.1016/j.cmpb.2013.08.015. Epub 2013 Sep 7.
Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.
计算机辅助检测（CAD）可以帮助放射科医生在早期发现肺部结节。在肺部结节 CAD 系统中，特征提取对于描述结节候选物的特征非常重要。在本文中，我们提出了一种新的基于三维形状的特征描述符，用于在 CT 扫描中检测肺部结节。在进行肺体积分割后，使用多尺度点增强滤波在分割的肺体积中检测结节候选物。然后，我们从检测到的结节候选物中提取特征描述符，并使用迭代壁消除方法对其进行细化。最后，使用基于支持向量机的分类器对结节和非结节进行分类。在 Lung Image Database Consortium 数据上评估了所提出系统的性能。该方法显著减少了结节候选物中的假阳性数量。该方法的灵敏度为 97.5%，每个扫描的假阳性率仅为 6.76。
