[Parallel Histogram Matching for Stacks] #Groovy #Fiji #ImageJ

Histogram_Matching_Parallel.groovy

//@File (label="Directory with file(s)",  style="directory" ) theDir
//@Integer (Label="Channel To Correct") ch_correct
//@Boolean (label="Use Normalization Parameters Below") is_normalize
//@Integer (Label="Minimum") the_min
//@Integer (Label="Maximum") the_max

/**
 * Image stack equalizer for image folder
 * 
 * The purpose of this script is to use the histogram of the first slice of the first image
 * in a folder as a reference and use Histogram Matching on all other slices in all other images
 * 
 * This was originally used to fix a flickering brightfield acquisition to make all images the same
 * brightness and contrast.
 * 
 * It normalizes the first image between the Minimum and Maximum value provided by the user before starting
 * if the checkbox is set.
 * Author: Olivier Burri, BioImage Analyst, BiImaging And Optics Platform (BIOP) 
 * 
 * Licensed under CC-BY-SA https://creativecommons.org/licenses/by-sa/4.0/
 */
 
import histogram2.HistogramMatcher
import ij.plugin.ChannelSplitter
import ij.plugin.RGBStackMerge
import ij.ImagePlus
import ij.IJ

import groovy.io.FileType
import groovyx.gpars.GParsExecutorsPool


import loci.plugins.BF;


// List all the files and keep only the ND ones
def allFilesList = []
// Get all the files in all the subfolders
theDir.eachFile(FileType.FILES) { file ->
	if(file.name.endsWith(".tif"))
		allFilesList << file
}


def savedir = new File(theDir.getAbsolutePath()+'/Equalized/')
savedir.mkdir()


def ref_hist

allFilesList.eachWithIndex{ file, i ->
	def imp = IJ.openImage(file.getAbsolutePath())
	
		print("\nProcessing Image "+imp.getTitle())
		String imageTitle = file.getName().substring(0,file.getName().size()-4)
		def image = imp;
		def all_images = []
		if(imp.getNChannels() > 1) {
			all_images = ChannelSplitter.split(imp)
			image = all_images[ch_correct-1] 
		}
			
		if (i == 0) {
			def ref_ip = image.getStack().getProcessor(1).convertToFloat().duplicate()
			if (is_normalize) { 
				def stats = ref_ip.getStatistics()
				ref_ip.subtract(stats.min)
				ref_ip.multiply(1/(stats.max-stats.min))
				ref_ip.multiply(the_max-the_min)
				ref_ip.add(the_min)
				
			}
			ref_ip = ref_ip.convertToShort(false)
			ref_hist = ref_ip.getHistogram()
		}
		def matchedImage= matchSliceHistograms(ref_hist, image)
		def newImage = matchedImage
		if(imp.getNChannels() > 1) {
			all_images[ch_correct-1] = matchedImage	
			newImage = RGBStackMerge.mergeChannels(all_images, false)
			newImage.setDisplayMode(imp.getDisplayMode())
			newImage.setLuts(imp.getLuts())
		} 
		

		// Make sure it is still the right hyperstack dimensions
		
		IJ.saveAs(newImage, "tif", savedir.getAbsolutePath()+"/"+imageTitle+".tif") 
		newImage.close()
}

return;

ImagePlus matchSliceHistograms(int[] refhist, ImagePlus imp) {
	def stack = imp.getStack()
	def final_imp = IJ.createHyperStack("Nobody cares",imp.getWidth(), imp.getHeight(), imp.getNChannels(), imp.getNSlices(), imp.getNFrames(), imp.getBitDepth())
	def matcher = new HistogramMatcher()
	def cores = Runtime.getRuntime().availableProcessors()
	GParsExecutorsPool.withPool(cores) {
		(1..stack.getSize()).eachParallel{
			print("\n\tProcessing Slice "+it)
			def hist_old = stack.getProcessor(it).getHistogram()
			def newHist = matcher.matchHistograms(hist_old, refhist)
			def ip = stack.getProcessor(it).duplicate()
			ip.applyTable(newHist)
			stack.setProcessor(ip, it)
		}
	}
	final_imp.setStack(stack)
	return final_imp
}

Hi Olivier,

Thank you for sharing this code. I was wondering if you have ever applied this code to an RGB image before? I have several images of florescent particles on a soil surface. The soil surface was imaged through time but as time progressed the brightness levels decrease in the images.
I'd like to use histogram matching to normalize the level of brightness for all of the images. I tried using your code in FIJI but it seems to influence each color channel different. For example, some pixels became over saturated and bright pixels became red. So I believe my issue is that it is increasing the brightness in one color channel and not the others. Is there a way to target the normalization to the overall brightness or increase the brightness of multiple channels?

Thank you for any help you can provide.

Emilee