| 基于三维形状指数的肺结节自动检测方法 |
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| 引用本文： | 董林佳,强彦,赵涓涓,原杰,赵文婷. 基于三维形状指数的肺结节自动检测方法[J]. 计算机应用, 2017, 37(11): 3182-3187. DOI: 10.11772/j.issn.1001-9081.2017.11.3182 |
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| 作者姓名： | 董林佳 强彦 赵涓涓 原杰 赵文婷 |
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| 作者单位： | 1. 太原理工大学 计算机科学与技术学院, 太原 030024;2. 山西省人民医院 CT室, 太原 030012 |
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| 基金项目： | 国家自然科学基金资助项目（61373100）；虚拟现实技术与系统国家重点实验室开放基金资助项目（BUAA-VR-17KF-14，BUAA-VR-17KF-15）；山西省回国留学人员科研资助项目（2016-038）。 |
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| 摘 要： | 针对在肺结节计算机辅助检测中存在误诊率、假阳性率较高，检测准确率较低等问题，提出一种基于三维形状指数和Hessian矩阵特征值构建类球形滤波器的结节检测方法。首先，提取肺实质区域，并计算各体素点Hessian矩阵的特征值和特征向量；其次，通过二维形状指数推导出三维形状指数公式，构建改进的三维类球形滤波器；最后，在三维肺实质区域内检测疑似结节区域，去除较多的假阳性区域，针对三维体数据上检测出结节所在位置，将检测到的坐标作为置信连接的多种子点输入，进行三维体数据分割，最终分割出三维结节。实验结果表明，所提算法能够有效地检测出不同类型的肺结节，对较难检测的磨玻璃结节也有较好的检测效果，结节检测的假阳性低，最终能达到92.36%的准确率和96.52%的敏感度。
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| 关 键 词： | 计算机辅助诊断 肺结节检测 Hessian 矩阵 形状指数 类球形滤波器 |
| 收稿时间： | 2017-05-16 |
| 修稿时间： | 2017-05-27 |
Automatic detection of pulmonary nodules based on 3D shape index |
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| DONG Linjia,QIANG Yan,ZHAO Juanjuan,YUAN jie,ZHAO Wenting. Automatic detection of pulmonary nodules based on 3D shape index[J]. Journal of Computer Applications, 2017, 37(11): 3182-3187. DOI: 10.11772/j.issn.1001-9081.2017.11.3182 |
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| Authors: | DONG Linjia QIANG Yan ZHAO Juanjuan YUAN jie ZHAO Wenting |
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| Affiliation: | 1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan Shanxi 030024, China;2. CT Room, Shanxi Provincial People's Hospital, Taiyuan Shanxi 030012, China |
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| Abstract: | Aiming at the problem of high misdiagnosis rate, high false positive rate and low detection accuracy in pulmonary nodule computer-aided detection, a method of nodular detection based on three-dimensional shape index and Hessian matrix eigenvalue was proposed. Firstly, the parenchyma region was extracted and the eigenvalues and eigenvectors of the Hessian matrix were calculated. Secondly, the three-dimensional shape index formula was deduced by the two-dimensional shape index, and the improved three-dimensional spherical like filter was constructed. Finally, in the parenchyma volume, the suspected nodule region was detected, and more false-positive regions were removed. The nodules were detected by the three-dimensional volume data, and the detected coordinates were input as the seeds of belief connect, and the three-dimensional data was splited to pick out three-dimensional nodules. The experimental results show that the proposed algorithm can effectively detect different types of pulmonary nodules, and has better detection effect on the ground glass nodules which are more difficult to detect, reduces the false positive rate of nodules, and finally reaches 92.36% accuracy rate and 96.52% sensitivity. |
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| Keywords: | computer-aided diagnosis pulmonary nodule detection Hessian matrix shape index like spherical filter |
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