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Box-Count Analysis of Two Dimensional Images: Methodology, Analysis and Classification

机译:二维图像的盒数分析:方法论,分析和分类

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This paper calls attention to the methodology issues of the box-counting method, precisely, to the scaling procedure and significance of the parameter calculated for different presentation of the same image. By using basic terms of fractal analysis and statistical assessment of correlation coefficients of a straight line fit, we showed correct choice for the size of boxes. Moreover, we showed correct box-count dimension in case of neurons with sparse or thick dendrites and small or large cell bodies. In addition, this paper presents our main results relating to the quantitative study and classification of 2D images from the monkey cerebral cortex and the human caudate nucleus.
机译:本文提请注意盒计数方法的方法论问题,准确地说,是针对同一图像的不同表示而计算出的参数的缩放过程和参数的重要性。通过使用分形分析的基本术语以及对直线拟合的相关系数的统计评估,我们为盒子的尺寸显示了正确的选择。此外,在神经元稀疏或浓密的树突以及小或大的细胞体的情况下,我们显示了正确的盒数尺寸。此外,本文介绍了与猴子大脑皮层和人尾状核的2D图像的定量研究和分类有关的主要结果。

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