Lee Jeong Won, Jeong Ji-Wook, Lee Sooyeul, Yoo Done-Sik, Kim Seunghwan
Electron. & Telecommun. Res. Inst., Daejeon, Korea.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:1983-5. doi: 10.1109/IEMBS.2006.260234.
The detection of abnormal lesions in the early stages of lung cancer is important to improve survival. Computer-aided detection (CAD) system can be useful for early detection of pulmonary nodules on computed tomography (CT) images for screening. Moreover, CAD system can be 'second opinion' when a radiologist detects the pulmonary nodules on multi-slice CT images. We developed a computer-aided detection system for pulmonary nodule detection on multi-detector row computed tomography (MDCT) images. We applied the nodule isolation technique using radial distribution function and additional algorithms. In this paper, we reported the ground-glass opacity (GGO) lesions detected by self-developed computer-aided pulmonary nodule detection system.
在肺癌早期检测异常病变对于提高生存率很重要。计算机辅助检测（CAD）系统有助于在计算机断层扫描（CT）图像上早期检测肺结节以进行筛查。此外，当放射科医生在多层CT图像上检测到肺结节时，CAD系统可以提供“第二意见”。我们开发了一种用于在多排探测器计算机断层扫描（MDCT）图像上检测肺结节的计算机辅助检测系统。我们应用了使用径向分布函数和附加算法的结节分离技术。在本文中，我们报告了由自行开发的计算机辅助肺结节检测系统检测到的磨玻璃密度（GGO）病变。
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