首页> 外文会议>Image Processing pt.1; Progress in Biomedical Optics and Imaging; vol.7 no.30 >Computerized detection of pulmonary nodules using a combination of 3D global and local shape information based on helical CT images
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Computerized detection of pulmonary nodules using a combination of 3D global and local shape information based on helical CT images

机译:基于螺旋CT图像结合3D整体和局部形状信息对肺结节进行计算机检测

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摘要

A novel method called local shape controlled voting has been developed for spherical object detection in 3D voxel images. By combining local shape properties into the global tracking procedure of normal overlap, the proposed method solved the ambiguities of normal overlap between a small size sphere and a possible large size cylinder, as the normal overlap technique can only measures the 'density' of normal overlapping, while how the normal vectors are distributed in 3D is not discovered. The proposed method was applied to computer aided detection of small size pulmonary nodules based on helical CT images. Experiments showed that this method attained a better performance compared to the original normal overlap technique.
机译:已经开发出一种称为局部形状控制投票的新颖方法,用于3D体素图像中的球形物体检测。通过将局部形状属性结合到法线重叠的全局跟踪过程中,该方法解决了小尺寸球体与可能的大尺寸圆柱体之间法线重叠的歧义,因为法线重叠技术只能测量法线重叠的“密度” ,但并未发现法线向量在3D中的分布方式。该方法被应用于基于螺旋CT图像的计算机辅助检测小尺寸肺结节。实验表明,与原始的正常重叠技术相比,该方法具有更好的性能。

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