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首页> 外文期刊>Microscopy and microanalysis: The official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada >Application of Fractal and Grey Level Co-Occurrence Matrix Analysis in Evaluation of Brain Corpus Callosum and Cingulum Architecture
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Application of Fractal and Grey Level Co-Occurrence Matrix Analysis in Evaluation of Brain Corpus Callosum and Cingulum Architecture

机译：分形和灰阶共现矩阵分析在脑Corp体和Cingulum体系评价中的应用

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摘要

This aim of this study was to assess the discriminatory value of fractal and grey level co-occurrence matrix (GLCM) analysis methods in standard microscopy analysis of two histologically similar brain white mass regions that have different nerve fiber orientation. A total of 160 digital micrographs of thionine-stained rat brain white mass were acquired using a Pro-MicroScan DEM-200 instrument. Eighty micrographs from the anterior corpus callosum and eighty from the anterior cingulum areas of the brain were analyzed. The micrographs were evaluated using the National Institutes of Health ImageJ software and its plugins. For each micrograph, seven parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, GLCM variance, fractal dimension, and lacunarity. Using the Receiver operating characteristic analysis, the highest discriminatory value was determined for inverse difference moment (IDM) (area under the receiver operating characteristic (ROC) curve equaled 0.925, and for the criterion IDM≤ 0.610 the sensitivity and specificity were 82.5 and 87.5%, respectively). Most of the other parameters also showed good sensitivity and specificity. The results indicate that GLCM and fractal analysis methods, when applied together in brain histology analysis, are highly capable of discriminating white mass structures that have different axonal orientation.
机译：本研究的目的是评估在具有不同神经纤维方向的两个组织学相似的脑白质区域的标准显微镜分析中，分形和灰度共生矩阵（GLCM）分析方法的鉴别价值。使用Pro-MicroScan DEM-200仪器共采集了160张硫氨酸染色的大鼠脑白质的数字显微照片。分析了来自前体的八十张显微照片和来自大脑前扣带区域的八十张显微照片。使用国立卫生研究院ImageJ软件及其插件对显微照片进行了评估。对于每个显微照片，计算了七个参数：角秒矩，反差矩，GLCM对比度，GLCM相关性，GLCM方差，分形维数和色差。使用接收器工作特性分析，确定了反差矩（IDM）的最高判别值（接收器工作特性（ROC）曲线下的面积等于0.925，对于标准IDM≤0.610的灵敏度和特异性分别为82.5和87.5％） ， 分别）。其他大多数参数也显示出良好的敏感性和特异性。结果表明，GLCM和分形分析方法一起应用于脑组织学分析时，具有很高的区分具有不同轴突方向的白色块状结构的能力。

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