SIMcheck.Rec_SAMismatch Maven / Gradle / Ivy
/*
* Copyright (c) 2015, Graeme Ball and Micron Oxford,
* University of Oxford, Department of Biochemistry.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see http://www.gnu.org/licenses/ .
*/
package SIMcheck;
import java.awt.Color;
import ij.*;
import ij.plugin.PlugIn;
import ij.gui.Plot;
import ij.process.*;
/** This plugin takes reconstructed data and produces produces plots of
* slice minimum and average feature intensity, as well as summarising standard
* deviation of the minimum to diagnose refractive index mismatch between
* the sample and PSF.
* @author Graeme Ball
*/
public class Rec_SAMismatch implements PlugIn, Executable {
public static final String name = "Spherical Aberration Mismatch";
public static final String TLA = "SAM";
private ResultSet results = new ResultSet(name, TLA);
public void run(String arg) {
ImagePlus imp = IJ.getImage();
exec(imp);
results.report();
}
/** Execute plugin functionality: plot slice minimum and feature mean
* scaled to stack mode as zero point, and report stdDev of minima.
* @param imps reconstructed data ImagePlus should be first imp
* @return ResultSet containing plots of mnimum and mean variation
*/
public ResultSet exec(ImagePlus... imps) {
IJ.showStatus(name + "...");
int nc = imps[0].getNChannels();
ImagePlus[] plots = new ImagePlus[nc];
for (int c = 1; c <= nc; c++) {
ImagePlus imp2 = singleChannel(imps[0], c);
StackStatistics stackStats = new StackStatistics(imp2);
int nz = imp2.getNSlices();
// build arrays of stats per Z plane
double[] sliceMinima = new double[nz];
double[] sliceMeans = new double[nz];
double[] zPlanes = new double[nz];
double plotMax = stackStats.min;
double plotMin = stackStats.max;
// commented out normalization to mode -- may add back in as option
// double stackMode = stackStats.dmode;
double stackMode = 0.0d;
for (int z = 0; z < nz; z++) {
zPlanes[z] = z + 1; // Z value for the plot (x-axis)
imp2.setSlice(z + 1);
ImageProcessor ip = imp2.getProcessor();
ImageStatistics stats = ip.getStatistics();
ImageStatistics featStats = I1l.featStats(ip);
// normalize all statistics to stack mode (not!) as 0 point
double nmin = stats.min - stackMode;
double nmean = featStats.mean - stackMode;
sliceMinima[z] = nmin;
sliceMeans[z] = nmean;
if (nmin < plotMin) {
plotMin = nmin;
}
if (nmean > plotMax) {
plotMax = nmean;
}
if (nmean < plotMin) {
plotMin = nmean;
}
}
Plot plot = new Plot(
"Reconstructed normalized min variation, C" + c,
"Z-section", "Intensity");
// display 20% beyond min and max
plotMin -= 0.2 * Math.abs(plotMin);
plotMax += 0.2 * Math.abs(plotMax);
plot.setLimits(1.0d, (double)nz, plotMin, plotMax);
plot.setLineWidth(2);
plot.setColor(Color.BLACK);
plot.addPoints(zPlanes, sliceMinima, Plot.LINE);
plot.setColor(Color.LIGHT_GRAY);
plot.addPoints(zPlanes, sliceMeans, Plot.LINE);
plot.setLineWidth(1);
ImagePlus impPlot = plot.getImagePlus();
I1l.drawPlotTitle(impPlot, "Reconstructed data intensity profile"
+ " (feature mean=gray, z-minimum=black)");
plots[c - 1] = impPlot;
double nstdev = Math.sqrt(J.variance(sliceMinima)) /
J.mean(sliceMeans);
results.addStat("C" + c + " Z-minimum variation", nstdev,
checkZmv(nstdev)); // FIXME, StatOK);
}
String title = I1l.makeTitle(imps[0], TLA);
ImagePlus impAllPlots = I1l.mergeChannels(title, plots);
impAllPlots.setDimensions(nc, 1, 1);
impAllPlots.setOpenAsHyperStack(true);
results.addImp("Z-section minimum (black) and mean feature intensity (gray)",
impAllPlots);
results.addInfo("How to interpret", "Z-minimum variation (ZMV)"
+ " is calculated as the"
+ " standard deviation of z-section minimum intensity"
+ " normalized to the average feature intensity. High ZMV"
+ " indicates spherical aberration mismatch between sample"
+ " and optical transfer function used for the reconstruction."
+ " Typically this is seen as dip in the minimum intensity"
+ " plot at the sample boundary. Note, that the absolute value"
+ " depends on image content.");
return results;
}
/** Is ZMV value acceptable? */
private static ResultSet.StatOK checkZmv(Double statValue) {
if (statValue < 0.3) {
return ResultSet.StatOK.YES;
} else if (statValue < 1.0) {
return ResultSet.StatOK.MAYBE;
} else {
return ResultSet.StatOK.NO;
}
}
/** Return a new ImagePlus containing a single channel of the input. */
private ImagePlus singleChannel(ImagePlus imp, int channel) {
int stackSize = imp.getStackSize();
int channels = imp.getNChannels();
ImageStack nuStack = new ImageStack(imp.getWidth(),imp.getHeight());
for (int s = 1; s <= stackSize; s++) {
if (((s % channels) == channel) ||
((channel == channels) && ((s % channels) == 0))) {
nuStack.addSlice(imp.getStack().getProcessor(s));
}
}
String nuTitle = imp.getShortTitle() + "_Ch" + Integer.toString(channel);
ImagePlus imp2 = new ImagePlus(nuTitle, nuStack);
imp2.setDimensions(1, imp.getNSlices(), imp.getNFrames()); // 1 channel
return imp2;
}
/** Interactive test method */
public static void main(String[] args) {
new ImageJ();
TestData.recon.show();
IJ.runPlugIn(Rec_SAMismatch.class.getName(), "");
}
}
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