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Segmentation-Based Interpolation of 3D Medical Images

机译:基于分割的3D医学图像插值

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摘要

This paper introduces a new interpolation algorithm based on images segmentation. Firstly, the algorithm obtains the regions of air, soft tissue and skeleton through segmenting images. Secondly, the algorithm uses matching interpolation in the same density regions, and scales the size of region as the interpolation data to interpolate image in the different density regions. The new image basically satisfies the requirements of medical image interpolation. Compared with linear interpolation, the new algorithm greatly improves the quality of image. The interpolation can be effectively used to construct 3D volume models.
机译:介绍一种基于图像分割的插值算法。首先,该算法通过分割图像获得空​​气,软组织和骨骼的区域。其次,该算法在相同密度区域中使用匹配插值,并缩放区域的大小作为插值数据,以在不同密度区域中插值图像。新图像基本满足医学图像插值的要求。与线性插值相比,新算法大大提高了图像质量。插值可有效地用于构建3D体积模型。

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