Chopin Joshua, Laga Hamid, Huang Chun Yuan, Heuer Sigrid, Miklavcic Stanley J
Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, South Australia, Australia.
The Australian Centre for Plant Functional Genomics, Urrbrae, South Australia, Australia.
PLoS One. 2015 Sep 23;10(9):e0137655. doi: 10.1371/journal.pone.0137655. eCollection 2015.
The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.
植物根解剖特征的形态是有效吸收水分和养分的关键因素。现有的根系解剖性状表型分析技术通常基于对根横截面显微图像的手动或半自动分割与标注。在本文中,我们提出了一种全自动工具,以下简称RootAnalyzer,用于从根横截面图像中高效提取和分析解剖性状。RootAnalyzer使用一系列图像处理技术,如局部阈值处理和最近邻识别,从图像背景中分割出植物根系,对皮层、中柱、内皮层和表皮进行分类和特征描述,随后生成有关根细胞和组织形态特性的统计数据。我们使用RootAnalyzer分析了15张小麦植株图像和1张玉米植株图像,并根据手动获取的真实数据评估其性能。比较结果表明,RootAnalyzer能够以超过90%的准确率充分表征大多数根组织区域。