Lu Yi, Huang Chenyang, Wang Jia, Shang Peng
School of Life Sciences, Northwestern Polytechnical University, 127 Youyi xilu, Xi'an, Shaanxi 710072, China.
School of Science, Northwestern Polytechnical University, 127 Youyi xilu, Xi'an, Shaanxi 710072, China.
ScientificWorldJournal. 2014 Mar 10;2014:637183. doi: 10.1155/2014/637183. eCollection 2014.
The arrangement of plant cortical microtubules can reflect the physiological state of cells. However, little attention has been paid to the image quantitative analysis of plant cortical microtubules so far. In this paper, Bidimensional Empirical Mode Decomposition (BEMD) algorithm was applied in the image preprocessing of the original microtubule image. And then Intrinsic Mode Function 1 (IMF1) image obtained by decomposition was selected to do the texture analysis based on Grey-Level Cooccurrence Matrix (GLCM) algorithm. Meanwhile, in order to further verify its reliability, the proposed texture analysis method was utilized to distinguish different images of Arabidopsis microtubules. The results showed that the effect of BEMD algorithm on edge preserving accompanied with noise reduction was positive, and the geometrical characteristic of the texture was obvious. Four texture parameters extracted by GLCM perfectly reflected the different arrangements between the two images of cortical microtubules. In summary, the results indicate that this method is feasible and effective for the image quantitative analysis of plant cortical microtubules. It not only provides a new quantitative approach for the comprehensive study of the role played by microtubules in cell life activities but also supplies references for other similar studies.
植物皮层微管的排列能够反映细胞的生理状态。然而，迄今为止，植物皮层微管的图像定量分析很少受到关注。本文将二维经验模态分解（BEMD）算法应用于原始微管图像的预处理。然后，选择通过分解得到的本征模态函数1（IMF1）图像，基于灰度共生矩阵（GLCM）算法进行纹理分析。同时，为了进一步验证其可靠性，利用所提出的纹理分析方法对拟南芥微管的不同图像进行区分。结果表明，BEMD算法在边缘保留和降噪方面具有积极作用，纹理的几何特征明显。GLCM提取的四个纹理参数完美地反映了皮层微管的两幅图像之间的不同排列。综上所述，结果表明该方法对植物皮层微管的图像定量分析是可行且有效的。它不仅为全面研究微管在细胞生命活动中的作用提供了一种新的定量方法，也为其他类似研究提供了参考。
