Liao Fuqi, Peng Jianling, Chen Rujin
Computing Services Department, Noble Research InstituteArdmore, OK, United States.
Noble Research InstituteArdmore, OK, United States.
Front Plant Sci. 2017 May 31;8:915. doi: 10.3389/fpls.2017.00915. eCollection 2017.
Diverse leaf forms ranging from simple to compound leaves are found in plants. It is known that the final leaf size and shape vary greatly in response to developmental and environmental changes. However, changes in leaf size and shape have been quantitatively characterized only in a limited number of species. Here, we report development of LeafletAnalyzer, an automated image analysis and classification software to analyze and classify blade and serration characteristics of trifoliate leaves in . The software processes high quality leaf images in an automated or manual fashion to generate size and shape parameters for both blades and serrations. In addition, it generates spectral components for each leaflets using elliptic Fourier transformation. Reconstruction studies show that the spectral components can be reliably used to rebuild the original leaflet images, with low, and middle and high frequency spectral components corresponding to the outline and serration of leaflets, respectively. The software uses artificial neutral network or -means classification method to classify leaflet groups that are developed either on successive nodes of stems within a genotype or among genotypes such as natural variants and developmental mutants. The automated feature of the software allows analysis of thousands of leaf samples within a short period of time, thus facilitating identification, comparison and classification of leaf groups based on leaflet size, shape and tooth features during leaf development, and among induced mutants and natural variants.
植物中存在从单叶到复叶的多种叶形。已知最终的叶片大小和形状会因发育和环境变化而有很大差异。然而,仅在有限数量的物种中对叶片大小和形状的变化进行了定量表征。在此,我们报告了LeafletAnalyzer的开发,这是一款用于分析和分类三出复叶叶片和锯齿特征的自动化图像分析与分类软件。该软件以自动或手动方式处理高质量的叶片图像,以生成叶片和锯齿的大小及形状参数。此外,它使用椭圆傅里叶变换为每个小叶生成光谱成分。重建研究表明,光谱成分可可靠地用于重建原始小叶图像,低频、中频和高频光谱成分分别对应小叶的轮廓和锯齿。该软件使用人工神经网络或K均值分类方法对在同一基因型茎的连续节点上发育的小叶组,或在自然变体和发育突变体等基因型之间发育的小叶组进行分类。该软件的自动化特性允许在短时间内分析数千个叶片样本,从而便于在叶片发育过程中以及在诱导突变体和自然变体之间,基于小叶大小、形状和齿特征对叶片组进行识别、比较和分类。