| CT图像中肺结节毛刺特征的计算机量化方法 |
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| 引用本文： | 王倩,龚建平,宋恩民,熊伟.CT图像中肺结节毛刺特征的计算机量化方法[J].华中科技大学学报(自然科学版),2011(7):26-29. |
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| 作者姓名： | 王倩 龚建平 宋恩民 熊伟 |
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| 作者单位： | 中南财经政法大学信息与安全工程学院;苏州大学附属第二医院影像诊断科;华中科技大学计算机科学与技术学院;江西中医学院计算机学院; |
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| 基金项目： | 苏州市科技发展计划(国际科技合作)资助项目(SWH0816); 中南财经政法大学引进人才振兴工程科研启动基金资助项目(31140911305) |
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| 摘 要： | 根据CT图像中肺结节毛刺的生长特点,提出了一种CT图像的肺结节毛刺特征计算机量化分级方法.该方法首先利用动态规划算法对感兴趣的CT图像区域进行肺结节分割,然后在分割的结节边界基础上,通过分析边界附近区域梯度方向的规律性,提取边界法线-梯度正交指数作为结节毛刺特征的量化指标,在此基础上对肺结节毛刺特征进行量化分级.实验结果表明:该指数能够准确地量化肺结节的毛刺特征,对区分无毛刺、短毛刺及长毛刺结节具有较高的分辨率.
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| 关 键 词： | 计算机量化 肺结节 CT图像 毛刺特征 分割 边界法线 梯度 |
Computerized quantification method for lung nodule spiculation features on CT images |
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| Wang Qian Gong Jianping Song Enmin, Xiong Wei.Computerized quantification method for lung nodule spiculation features on CT images[J].JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE,2011(7):26-29. |
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| Authors: | Wang Qian Gong Jianping Song Enmin Xiong Wei |
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| Institution: | Wang Qian1 Gong Jianping2 Song Enmin3,4 Xiong Wei5(1 School of Information and Safety Engineering,Zhongnan University of Economics and Law,Wuhan 430073,China,2 Radiodiagnosis Department,The Second Affiliated Hospital of Soochow University,Suzhou 215004,Jiangsu China,3 College of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,4 School of Computer,Jiangxi University of Traditional Chinese Medicine,Nanchang 330004,5 Center of Computing and Experimenting... |
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| Abstract: | An approach to quantify the lung nodule speculation levels on CT(computed tomography) images was proposed.The lung nodule was segmented by using the dynamic programming algorithm on the region of the interest.Based on the segmented boundary and the regularity of the gradients,a new index describing the perpendicularity between the normal vectors of the boundary and the gradients nearby the boundary was extracted to quantify the spiculation levels of the lung nodule.The experiment results indicate that this ... |
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| Keywords: | computerized quantification lung nodules CT(computed tomography) spiculation features segmentation normal vectors of boundary gradient |
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