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通过灰度粒度分析对植物组织切片中的细胞形态进行参数映射。

Parametric mapping of cellular morphology in plant tissue sections by gray level granulometry.

作者信息

Legland David, Guillon Fabienne, Devaux Marie-Françoise

机构信息

UR1268 Biopolymères, Interactions et Assemblages, INRAE, Nantes, France.

出版信息

Plant Methods. 2020 May 6;16:63. doi: 10.1186/s13007-020-00603-7. eCollection 2020.

Abstract

BACKGROUND

The cellular morphology of plant organs is strongly related to other physical properties such as shape, size, growth, mechanical properties or chemical composition. Cell morphology often vary depending on the type of tissue, or on the distance to a specific tissue. A common challenge in quantitative plant histology is to quantify not only the cellular morphology, but also its variations within the image or the organ. Image texture analysis is a fundamental tool in many areas of image analysis, that was proven efficient for plant histology, but at the scale of the whole image.

RESULTS

This work presents a method that generates a parametric mapping of cellular morphology within images of plant tissues. It is based on gray level granulometry from mathematical morphology for extracting image texture features, and on Centroidal Voronoi Diagram for generating a partition of the image. Resulting granulometric curves can be interpreted either through multivariate data analysis or by using summary features corresponding to the local average cell size. The resulting parametric maps describe the variations of cellular morphology within the organ.

CONCLUSIONS

We propose a methodology for the quantification of cellular morphology and of its variations within images of tissue sections. The results should help understanding how the cellular morphology is related to genotypic and / or environmental variations, and clarify the relationships between cellular morphology and chemical composition of cell walls.

摘要

背景

植物器官的细胞形态与其他物理特性密切相关,如形状、大小、生长、机械性能或化学成分。细胞形态通常因组织类型或与特定组织的距离而异。定量植物组织学中的一个常见挑战是不仅要量化细胞形态,还要量化其在图像或器官内的变化。图像纹理分析是图像分析许多领域的基本工具,已被证明在植物组织学中有效,但仅适用于整个图像的尺度。

结果

本文提出了一种在植物组织图像中生成细胞形态参数映射的方法。该方法基于数学形态学的灰度粒度分析来提取图像纹理特征,并基于质心 Voronoi 图来生成图像的划分。所得的粒度曲线可以通过多变量数据分析或使用对应于局部平均细胞大小的汇总特征来解释。所得的参数映射描述了器官内细胞形态的变化。

结论

我们提出了一种用于量化组织切片图像中细胞形态及其变化的方法。这些结果应有助于理解细胞形态如何与基因型和/或环境变化相关,并阐明细胞形态与细胞壁化学成分之间的关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2403/7201695/65f236fea27f/13007_2020_603_Fig1_HTML.jpg

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