Output saving

Prerequisites
Before starting this lesson, you should be familiar with:

Binarize an image

Connected components labeling

Simple 2D object analysis

Generating results tables

Defining regions of interest (ROIs)

Learning Objectives
After completing this lesson, learners should be able to:

Save measurements as a table

Save ROIs

Save output label mask

Motivation

There are multiple situations in which you need to save the different types of output you can generate with your image analysis pipelines. For example, you may want to save your results as measurement tables for further analysis in other software (e.g. RStudio, MS EXCEL, ..). It can also be important to save the regions of interest (ROIs) that were used for particular measurements, so that you can look back at them for reference, or to use them for visualization purposes. In addition to ROIs, images can be saved as label masks to store the information on different regions. Finally, you may also want to save your entire script containing particular settings or parameters that you used, so that you can re-run the analysis with the exact same settings or compare it with the results obtained using different settings.

Concept map

graph TD II("Input image") --> IA("Image analysis process") IA --> RS("Measurements table") IA --> ROI("ROIs") IA --> PM("Label mask")

Figure

Image analysis processes can yield several outputs, such as a) label masks, b) measurement tables, or c) regions of interest (ROIs), which may include different types, such as polygon outlines, lines or points.

Activities

Use a template script and run it.
Understand what the commands do.
Record yourself saving the output.
Edit the script using the recorded commands.
Optional: define an output directory as a parameter and use string concatenation to specify where to save the file and with what name. An example output directory could be ‘C:\\Users\\username\\Desktop’ on Windows (note double “\” because a single “\” is interpreted as an escape character) or ‘/Users/username/Desktop/’ on MacOS.

Show activity for:

ImageJ Macro

Download the template script: output_saving_imagej-macro_template.ijm. The aim of the script is to generate different kinds of output (labels, results, ROIs) from this image: xy_8bit_binary_randomshapes.tif.
Run and understand the script.
Add commands that save the different output: label image, results table, ROIs.

Solution

// This macro uses the particle analyzer to measure features of shapes.
// Different outputs are saved: ROIs, results table, and label mask.

// make sure the background is set to black in Process>Binary>Options
run("Options...", "iterations=1 count=1 black do=Nothing");

// specify an output directory
outputDir = FIXME // (e.g. 'C:\\Users\\username\\Desktop' or 'C:/Users/username/Desktop' on Windows, or '/Users/username/Desktop/' on MacOS)

// specify size parameters for object selection
minSize = 0
maxSize = 1000

// close any pre-existing output you do not want in your saving results
roiManager("reset"); // clear any pre-existing ROIs
run("Clear Results"); // clear any pre-existing results
run("Close All"); // close any open images

// Set measurements and run particle analyzer on binary shapes image
run("Set Measurements...", "area centroid center perimeter bounding redirect=None decimal=3") // set desired measurements
open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary_randomshapes.tif"); // open binary image with random shapes
run("Analyze Particles...", "size=&minSize-&maxSize show=[Count Masks] display add") // run the particle analyzer>run("glasbey")

// Save the results
saveAs("tiff", outputDir + File.separator + "shapes_labels_macro.tiff"); // save label mask to output directory
saveAs("results", outputDir + File.separator + "shapes_results_macro.txt"); // save results file to output directory
roiManager("Save", outputDir + File.separator + "shapes_ROIset_macro.zip"); // save rois to output directory

ImageJ Jython

Download the template script: output_saving_imagej-jython_template.py. The aim of the script is to generate different kinds of output (labels, results, ROIs) from this image: xy_8bit_binary_randomshapes.tif.
Run and understand the script.
Add commands that save the different output: label image, results table, ROIs.

Solution

# import classes
from ij import IJ, ImagePlus, WindowManager
from ij.io import FileSaver
from ij.plugin.filter import ParticleAnalyzer
from ij.plugin.frame import RoiManager
from ij.measure import ResultsTable, Measurements
from ij.process import ImageProcessor
import os

# make sure the background is set to black in Process>Binary>Options
IJ.run("Options...", "iterations=1 count=1 black")

# Specify an output directory
outputDir = FIXME # (e.g. r'C:\Users\username\Desktop', 'C:\\Users\\username\\Desktop' or 'C:/Users/username/Desktop' on Windows or '/Users/username/Desktop/' on MacOS)

# Specify size parameters for object selection
min_size = 0
max_size = 1000

# Initialize Roi Manager and empty results table, close other open images
rm = RoiManager().getInstance()
rm.reset()
IJ.run("Close All")

# Open binary shapes image
shapes = IJ.openImage("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary_randomshapes.tif")

# Configure and run particle analyzer
results = ResultsTable() # construct empty resultstable
pa = ParticleAnalyzer((ParticleAnalyzer.ADD_TO_MANAGER + ParticleAnalyzer.SHOW_ROI_MASKS),(Measurements.AREA + Measurements.CENTROID + Measurements.CENTER_OF_MASS + Measurements.PERIMETER + Measurements.RECT), results, min_size, max_size, 0, 1)
pa.analyze(shapes) # run the particle analyzer on the image
results.show("Results")

# Save label mask, results table, and ROIs

labelMask = WindowManager.getImage("Count Masks of xy_8bit_binary_randomshapes.tif")
IJ.run(labelMask, "Glasbey", "") # set glasbey LUT
FileSaver(labelMask).saveAsTiff(os.path.join(outputDir, "shapes_labels_jython.tif")) # save the label mask

results.save(os.path.join(outputDir, "shapes_results_jython.txt")) # save results table

rm.runCommand("Save", os.path.join(outputDir, "shapes_ROIset_jython.zip")) # save the ROIs

Exercises

Show exercise/solution for:

ImageJ Macro

Adapt the code from the activity such that you:

Specify an output directory in the beginning
Save the results table as comma-separated data table instead of text-delimited data.
Save the output label image in a different image format (e.g. PNG, JPEG). Is this a good format for label images?

Solution

// This macro uses the particle analyzer to measure features of shapes.
// Different outputs are saved: ROIs, results table, and label mask.

// make sure the background is set to black in Process>Binary>Options
run("Options...", "iterations=1 count=1 black do=Nothing");

// specify an output directory
outputDir = FIXME // (e.g. 'C:\\Users\\username\\Desktop' or 'C:/Users/username/Desktop' on Windows, or '/Users/username/Desktop/' on MacOS)

// specify size parameters for object selection
minSize = 0
maxSize = 1000

// close any pre-existing output you do not want in your saving results
roiManager("reset"); // clear any pre-existing ROIs
run("Clear Results"); // clear any pre-existing results
run("Close All"); // close any open images

// Set measurements and run particle analyzer on binary shapes image
run("Set Measurements...", "area centroid center perimeter bounding redirect=None decimal=3") // set desired measurements
open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary_randomshapes.tif"); // open binary image with random shapes
run("Analyze Particles...", "size=&minSize-&maxSize show=[Count Masks] display add") // run the particle analyzer>run("glasbey")

// Save the results
saveAs("Png", outputDir + File.separator + "/shapes_labels_macro.png"); // save label mask to output directory
saveAs("Results", outputDir + File.separator + "/shapes_results_macro.csv"); // save results file to output directory
roiManager("Save", outputDir + File.separator + "/shapes_ROIset_macro.zip"); // save ROIs to output directory

ImageJ Jython

Adapt the code from the activity such that you:

Specify an output directory in the beginning
Save the results table as comma-separated data table instead of text-delimited data.
Save the output label image in a different image format (e.g. PNG, JPEG). Is this a good format for label images?

Solution

# import classes
from ij import IJ, ImagePlus, WindowManager
from ij.io import FileSaver
from ij.plugin.filter import ParticleAnalyzer
from ij.plugin.frame import RoiManager
from ij.measure import ResultsTable, Measurements
from ij.process import ImageProcessor
import os

# make sure the background is set to black in Process>Binary>Options
IJ.run("Options...", "iterations=1 count=1 black")

# Specify an output directory
outputDir = FIXME # (e.g. r'C:\Users\username\Desktop', 'C:\\Users\\username\\Desktop' or 'C:/Users/username/Desktop' on Windows or '/Users/username/Desktop/' on MacOS)

# Specify size parameters for object selection
min_size = 0
max_size = 1000

# Initialize Roi Manager and empty results table, close other open images
rm = RoiManager().getInstance()
rm.reset()
IJ.run("Close All")

# Open binary shapes image
shapes = IJ.openImage("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary_randomshapes.tif")

# Configure and run particle analyzer
results = ResultsTable() # construct empty resultstable
pa = ParticleAnalyzer((ParticleAnalyzer.ADD_TO_MANAGER + ParticleAnalyzer.SHOW_ROI_MASKS),(Measurements.AREA + Measurements.CENTROID + Measurements.CENTER_OF_MASS + Measurements.PERIMETER + Measurements.RECT), results, min_size, max_size, 0, 1)
pa.analyze(shapes) # run the particle analyzer on the image
results.show("Results")

# Save results, label mask, and ROIs
labelMask = WindowManager.getImage("Count Masks of xy_8bit_binary_randomshapes.tif")
IJ.run(labelMask, "Glasbey", "") # set glasbey LUT
FileSaver(labelMask).saveAsPng(os.path.join(outputDir, "shapes_labels_jython.png")) # save the label mask

results.save(os.path.join(outputDir, "shapes_results_jython.csv")) # save results table

rm.runCommand("Save", os.path.join(outputDir, "shapes_ROIset_jython.zip")) # save the ROIs

Assessment

Fill in the blanks

Three commonly saved data output types include ___ .
In order to safely re-open your data or open it in different software, you need to save in an __ file format.

Solution

Three commonly saved data output types include results tables, ROI sets, and label masks.

In order to safely re-open your data or open it in different software, you need to save in an interoperable file format.

True or False

Label masks should be saved in JPEG format.
Tab-delimited text is a decent output format for results tables.

Solution

Label masks should be saved in JPEG format. False (JPEG compression results in loss of the unique label values in the image)

Tab-delimited text is a decent output format for results tables. True (this is generally more stable in other software than for example comma-delimited data)

Explanations

Follow-up material

Recommended follow-up modules:

Learn more: