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Bone volume measurement of human femur head specimens by modeling the histograms of micro-CT images

机译:通过模拟微型CT图像直方图的人股骨头标本的骨骼体积测量

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Micro-CT analysis is a powerful technique for bone characterization. It allows to obtain histo-morphometric parameters of bone sample. To do this, some different levels of data processing are required, a binarization of the images is usually performed as first step. Binarization consists in establishing a gray-level threshold in order to assign each pixel to bone or void spaces. A wrong choice of this threshold induces an overestimation or underestimation of the parameters. The main problem is represented by the quantization error due to spatial sampling, causing partial volume artifacts. Each voxel which samples the external surface of the tissue represents an average of both air and bone and the simple thresholding process operates an incorrect discrimination of the two materials. To overcome this limitation, the aim of this study is the extraction of bone volumetric information by fitting and reconstruction of the grey-levels histogram with a suitable set of functions.
机译:微型CT分析是一种强大的骨骼表征技术。它允许获得骨骼样本的组织形态学参数。为此,需要一些不同的数据处理级别,通常作为第一步执行图像的二值化。二值化由建立灰度级阈值,以便将每个像素分配给骨骼或空隙空间。错误选择此阈值会引起参数的高度估计或低估。主要问题是由于空间采样引起的量化误差表示,导致部分卷伪像。样本组织外表面的每个体素表示空气和骨骼的平均值,并且简单的阈值处理过程操作两种材料的不正确辨别。为了克服这一限制,本研究的目的是通过用合适的功能组合拟合和重建灰度直方图来提取骨体积信息。

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