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A cytoskeletal injury classifier based on 'spectrum enhancement' and data fusion

机译:基于“频谱增强”和数据融合的细胞骨架损伤分类器

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The morphology of cytoskeletal microtubules has been analyzed by fractal, direct and spectral methods. Sets of images were obtained from the epifluorescence microscopy of primary cultures of rat hepatocytes treated with fungicide concentrations of SO and 25 μg/ml for 2h. The morphological descriptors extracted by said methods included contour and mass fractal dimension, total variation, the L~1-norm of the Laplacian and properties of the "enhanced spectrum". The latter is obtained by suitably processing the logarithm of power spectral density with the aim of separating image structure (low spatial frequency) from texture (high spatial frequency). Descriptors were fused by principal components analysis. A classification algorithm was trained to tell undisturbed (control) cytoskeletal structures from those treated at the higher dose. The eigenvector matrix of the trained classifier was used to rank structures treated at the lower dose: from regression on the set centroid coordinates a tentative relation between the first principal component (the "response") and dose has been obtained. The same ranking procedure was applied to structures recovering from injury (24h after exposure to the higher dose) and the extent of recovery has been quantified. The paper includes a possible interpretation of some morphological descriptors and their role in automatic classification.
机译:通过分形,直接和光谱方法分析了细胞骨架微管的形态。从用杀菌剂浓度的SO和25μg/ ml处理2h的大鼠肝细胞原代培养物的表面荧光显微镜检查获得的图像集。通过所述方法提取的形态学描述符包括轮廓和质量分形维数,总变化,拉普拉斯算子的L-1范数和“增强谱”的性质。后者是通过适当地处理功率谱密度的对数而获得的,目的是将图像结构(低空间频率)与纹理(高空间频率)分离。描述符通过主成分分析进行融合。训练了分类算法,以区分高剂量治疗的细胞骨架结构(对照)。训练有素的分类器的特征向量矩阵用于对以较低剂量处理的结构进行排名:通过对设定质心坐标的回归,获得了第一主要成分(“响应”)和剂量之间的暂时关系。将相同的分级程序应用于从损伤中恢复的结构(暴露于较高剂量后24小时),并且恢复程度已量化。本文包括一些形态学描述符的可能解释及其在自动分类中的作用。

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