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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图像的3D全局和局部形状信息的组合计算机化检测肺结核

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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:已经开发了一种新的方法,用于3D体素图像中的球面对象检测。通过将局部形状属性组合到正常重叠的全局跟踪过程中,所提出的方法解决了小尺寸球体和可能的大尺寸气缸之间的正常重叠的模糊性,因为正常的重叠技术只能测量正常重叠的“密度” ,虽然如何在3D中分配正常载体。基于螺旋CT图像,将所提出的方法应用于计算机辅助检测小尺寸肺结节。实验表明,与原始正常重叠技术相比,该方法达到了更好的性能。

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