Perpendicular to both the second and third principal axes. The 3D coordinates of the first principal axis of the ROI shape. merge: if file exists “Overwrite” (to replace the file), “Merge sheets” (to merge files when processing files in batch), or “Merge sheets excluding first row” (also to merge files but without repeating the first row, which usually contains the name of the columns).output file: opens a dialog box, in which you need to provide the full path to save the data (including the file name).format: “Spreadsheet” or “Text” to export the data to.The spreadsheet can be saved to disk using the “Workbook to file” block with the following options: The block generates a spreadsheet with a single sheet or “workbook” named “ROI Statistics”. Move a descriptor up or down the list: click on the name of the descriptor to select it, then click on the arrows in the upper right corner. Click on “Columns to display” to re-order unticked descriptors. Tick the box on the upper left corner to tick/untick all descriptors. on the right: what is displayed in the tab.Click on the wheel to choose the measurements to display in the tab and the ones to export.Ĭlicking on the wheel displays two tables: Click on the spreasheet with an arrow to export the measurements. It automatically stores measurements from ROIs. The ROI tab is located in the side pane on the right side of the graphical user interface (GUI) or Icy. To make plots, click on the three stacked lines next to the floppy disk to display histograms or scatterplots of the measurements.Note that the plugin generates a spreadsheet view with one sheet per opened image. To save the measurements, click on the floppy disk and select “Export as text file” or “Export as Excel file”, this open a dialog box with which you can indicate the path to save the data (folder and name of the file).To compute measurements on ROIs, click on “Select features…” and tick the measurements you would like to compute.Type “roistatistics” in the search bar of Icy to access it. We detail below the specificities of each mode and give the definition of each descriptor. Note that the name of each sheet is inserted inside the text file before the actual sheet contents, with beginning and ending “=” symbol (e.g. Preferred format to process further the data with R or Python programming languages. Text: produces a “.txt” file, where each cell is delimited with a tabulation character (“t”).Note that the stored color may differ slightly from the actual ROI color, due to XLS format restrictions on color palettes. For the ROI color descriptor, the color of the ROI is used to fill the background of the cell and the cell is filled with the hexadecimal code (see ROI blocks for more info on the ROI color). Spreadsheet: produces a “.xlsx” file, compatible with most spreadsheet software.As a tab called “ROI tab” in the side pane on the right of the GUI of Icy. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.This plugin computes measurements (also called descriptors or features) on the Regions Of Interest (ROI), and exports these measures to a text or spreadsheet file. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. SHAPE, LAMINA, LeafAnalyzer, LEAFPROCESSOR) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices. Canopy shade is an important environmental cue that affects plant architecture and life history the suite of responses is collectively called the shade avoidance syndrome (SAS). For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools. Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review and recent examples).
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