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

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.

Darkfield and Fluorescence Macrovision of a Series of Large Images to Assess Anatomical and Chemical Tissue Variability in Whole Cross-Sections of Maize Stems

“…Cells were found to be smaller in the parenchyma near the rind, which was also measured in Legland et al (2020) , who computed local granulometric curves in a section of maize stem. M09 and M14 showed the smallest diameters of approximately 40 μm, and M01 showed the largest diameter of approximately 55 μm.…”

“…Areas were obtained by pixel counting; perimeters, vascular bundle length and width were obtained using the regionprops MATLAB function. Rind thickness was evaluated by granulometry using mathematical morphology transformations ( Soille, 2003 ; Devaux et al, 2008 ; Legland et al, 2014 , 2020 ); see “Parenchyma Cell Size” for an introduction to the method. The thickness distributions were obtained by applying opening transformations using squared structuring elements and a maximum size of 1,456 μm.…”

“…Cell size was measured from the BlueBox greyscale image on the two parenchyma regions. Grey-level granulometry developed using mathematical morphology ( Soille, 2003 ) was applied without segmenting the cells as described in Devaux et al (2008) and Legland et al (2020) . Grey-level granulometry consists of successively applying size transformations of the image through a mask of known geometry, called a structuring element ( Soille, 2003 ).…”

“…In addition to tissue segmentation, Devaux and Legland (2014) proposed applying grey-level granulometry using morphological closings to directly extract cell size distributions from grey-level images. Legland et al (2020) used the method on maize stem images to compute the parametric maps of cell size.…”

“…In the case of maize stems, the stem section area can be of 1-2 cm 2 while the cells diameter can be of approximately 60 µm (Zhang et al, 2013;Legland et al, 2014Legland et al, , 2017; moreover, the experiments often need to repeated to tackle the biological variability. For a few years, whole stem section imaging has been developed (Zhang et al, 2013(Zhang et al, , 2019Legland et al, 2014Legland et al, , 2017Legland et al, , 2020Heckwolf et al, 2015;Perrier et al, 2017). Images of hand-or microtome-cut stem cross-sections were acquired with either a macroscope, a microscope slide scanner or a flatbed scanner.…”