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CO-OCCURRENCE MATRICES FOR VOLUMETRIC DATA

机译:体积数据的共生矩阵

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In this paper, we investigate a new approach to the cooccurrence matrix currently used to extract textural features: co-occurrence matrices for volumetric data. While traditional texture metrics have concentrated on 2D texture, 3D imaging modalities are becoming more and more prevalent, providing the possibility of examining texture as a volumetric phenomenon. Just as computer graphics have used 3D textures as a more realistic alternative to 2D texture mapping, we expect that texture derived from volumetric data will have better discriminating power than 2D texture derived from slice data. An experimental study has been conducted in which the results for textural features derived from 2D are compared to those results derived from using cooccurrence matrices for volumetric data. Our preliminary experimental results indicate that the volumetric texture features have better discriminating power than 2D texture derived from slice data.
机译:在本文中,我们研究了一种用于当前用于提取纹理特征的共现矩阵的新方法:用于体积数据的共现矩阵。尽管传统的纹理度量标准集中于2D纹理,但是3D成像模态正变得越来越普遍,从而提供了将纹理作为体积现象进行检查的可能性。正如计算机图形已将3D纹理用作2D纹理映射的更现实的替代方法一样,我们期望从体积数据派生的纹理比从切片数据派生的2D纹理具有更好的辨别力。已经进行了一项实验研究,其中将源自2D的纹理特征的结果与源自使用共现矩阵获取体积数据的结果进行比较。我们的初步实验结果表明,与从切片数据得出的2D纹理相比,体积纹理特征具有更好的识别能力。

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