An Image Skeletonization-Based Tool for Pollen Tube Morphology Analysis and Phenotyping  

作　　者：Chaofeng Wang Cai-Pinq Gui Hai-Kuan Liu Dong Zhang Axel Mosig 

机构地区：[1]CAS-MPG Partner InStitute and CAS'Key Laboratory for Computational BiolOgy, Shanghai Institutes for Biological Sciences, The ChineseAcademy of Sciences, Shanghai 200031, China [2]National Key Laboratory of Plant Molecular Genetics, Institute of Plant Physiology and Ecology, Shanghai Institutes for BiologicalSciences, The Chinese Academy of Sciences, Shanghai 200032, China [3]Department of Biology and Biotechnology, Ruhr University Bochum, Bochum 44801, Germany

出　　处：《Journal of Integrative Plant Biology》2013年第2期131-141,共11页植物学报（英文版）

基　　金：supported by a Chinese Academy of Sciences Visiting Professorship for Senior International Scientists (2011T1S11) to A.M.;the National Natural Science Foundation of China (30970266) to D.Z

摘　　要：The mechanism underlying pollen tube growth involves diverse genes and molecular pathways. Alterations in the regulatory genes or pathways cause phenotypic changes reflected by cellular morphology, which can be captured using fluorescence microscopy. Determining and classifying pollen tube morphological phenotypes in such microscopic images is key to our understanding the involvement of genes and pathways. In this context, we propose a computational method to extract quantitative morphological features, and demonstrate that these features reflect morphological differences relevant to distinguish different defects of pollen tube growth. The corresponding software tool furthermore includes a novel semi-automated image segmentation approach, allowing to highly accurately identify the boundary of a pollen tube in a microscopic image.The mechanism underlying pollen tube growth involves diverse genes and molecular pathways. Alterations in the regulatory genes or pathways cause phenotypic changes reflected by cellular morphology, which can be captured using fluorescence microscopy. Determining and classifying pollen tube morphological phenotypes in such microscopic images is key to our understanding the involvement of genes and pathways. In this context, we propose a computational method to extract quantitative morphological features, and demonstrate that these features reflect morphological differences relevant to distinguish different defects of pollen tube growth. The corresponding software tool furthermore includes a novel semi-automated image segmentation approach, allowing to highly accurately identify the boundary of a pollen tube in a microscopic image.

关 键 词：Cell morphology image skeletonization pollen tube growth branching image based phenotyping. 

分 类 号：Q944.42[生物学—植物学]