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io.github.mianalysis.mia.process.imagej.AutoLocalThreshold3D Maven / Gradle / Ivy
package io.github.mianalysis.mia.process.imagej;
import ij.IJ;
import ij.ImagePlus;
import ij.plugin.Duplicator;
import ij.plugin.Filters3D;
import ij.plugin.PlugIn;
import ij.process.ImageConverter;
/**
* Created by sc13967 on 14/11/2017.
*/
public class AutoLocalThreshold3D implements PlugIn {
public static final String BERNSEN = "Bernsen";
public static final String CONTRAST = "Contrast";
public static final String MEAN = "Mean";
public static final String MEDIAN = "Median";
public static final String PHANSALKAR = "Phansalkar";
private double lowerThreshold = Double.MIN_VALUE;
public void exec(ImagePlus ipl, String myMethod, int radiusXY, int radiusZ, double thrMult, double par1, double par2, boolean doIwhite) {
switch(myMethod) {
case BERNSEN:
Bernsen(ipl,radiusXY,radiusZ,thrMult,par1,par2,doIwhite);
break;
case CONTRAST:
Contrast(ipl,radiusXY,radiusZ,thrMult,par1,par2,doIwhite);
break;
case MEAN:
Mean(ipl,radiusXY,radiusZ,thrMult,par1,par2,doIwhite);
break;
case MEDIAN:
Median(ipl,radiusXY,radiusZ,thrMult,par1,par2,doIwhite);
break;
case PHANSALKAR:
Phansalkar(ipl,radiusXY,radiusZ,thrMult,par1,par2,doIwhite);
break;
}
}
private void Bernsen(ImagePlus ipl, int radiusXY, int radiusZ, double thrMult, double par1, double par2, boolean doIwhite ) {
// Bernsen recommends WIN_SIZE = 31 and CONTRAST_THRESHOLD = 15.
// 1) Bernsen J. (1986) "Dynamic Thresholding of Grey-Level Images"
// Proc. of the 8th Int. Conf. on Pattern Recognition, pp. 1251-1255
// 2) Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
// Techniques and Quantitative Performance Evaluation" Journal of
// Electronic Imaging, 13(1): 146-165
// http://citeseer.ist.psu.edu/sezgin04survey.html
// Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
// This version uses a circular local window, instead of a rectagular one
ImagePlus maxIpl, minIpl, oriIpl;
int contrast_threshold = (int) Math.round(15*thrMult);
int local_contrast;
int mid_gray;
byte object;
byte backg;
int temp;
if (par1!=0) contrast_threshold= (int) par1;
if (doIwhite){
object = (byte) 0xff;
backg = (byte) 0;
}
else {
object = (byte) 0;
backg = (byte) 0xff;
}
oriIpl = new Duplicator().run(ipl);
IJ.run(oriIpl,"32-bit",null);
IntensityMinMax.run(ipl,true);
IJ.run(ipl,"8-bit",null);
// Creating maximum image
maxIpl = new Duplicator().run(ipl);
maxIpl.setStack(Filters3D.filter(maxIpl.getImageStack(),Filters3D.MAX,radiusXY,radiusXY,radiusZ));
// Creating minimum image
minIpl = new Duplicator().run(ipl);
minIpl.setStack(Filters3D.filter(minIpl.getImageStack(),Filters3D.MIN,radiusXY,radiusXY,radiusZ));
for (int z = 1; z <= ipl.getNSlices(); z++) {
for (int c = 1; c <= ipl.getNChannels(); c++) {
for (int t = 1; t <= ipl.getNFrames(); t++) {
ipl.setPosition(c, z, t);
oriIpl.setPosition(c, z, t);
maxIpl.setPosition(c, z, t);
minIpl.setPosition(c, z, t);
byte[] pixels = (byte[]) ipl.getProcessor().getPixels();
float[] ori = (float[]) oriIpl.getProcessor().getPixels();
byte[] max = (byte[]) maxIpl.getProcessor().getPixels();
byte[] min = (byte[]) minIpl.getProcessor().getPixels();
for (int i = 0; i < pixels.length; i++) {
local_contrast = ((max[i] & 0xff) - (min[i] & 0xff));
mid_gray = ((min[i] & 0xff) + (max[i] & 0xff)) / 2;
temp = (pixels[i] & 0x0000ff);
if (local_contrast < contrast_threshold)
pixels[i] = (mid_gray >= 128 & ori[i] > lowerThreshold) ? object : backg; //Low contrast region
else
pixels[i] = (temp >= mid_gray & ori[i] > lowerThreshold) ? object : backg;
}
}
}
}
ipl.setPosition(1,1,1);
}
private void Contrast(ImagePlus ipl, int radiusXY, int radiusZ, double thrMult, double par1, double par2, boolean doIwhite) {
// G. Landini, 2013
// Based on a simple contrast toggle. This procedure does not have user-provided paramters other than the kernel radius
// Sets the pixel value to either white or black depending on whether its current value is closest to the local Max or Min respectively
// The procedure is similar to Toggle Contrast Enhancement (see Soille, Morphological Image Analysis (2004), p. 259
ImagePlus maxIpl, minIpl, oriIpl;
byte object;
byte backg;
if (doIwhite){
object = (byte) 0xff;
backg = (byte) 0;
}
else {
object = (byte) 0;
backg = (byte) 0xff;
}
oriIpl = new Duplicator().run(ipl);
IJ.run(oriIpl,"32-bit",null);
IntensityMinMax.run(ipl,true);
IJ.run(ipl,"8-bit",null);
// Creating maximum image
maxIpl = new Duplicator().run(ipl);
maxIpl.setStack(Filters3D.filter(maxIpl.getImageStack(),Filters3D.MAX,radiusXY,radiusXY,radiusZ));
// Creating minimum image
minIpl = new Duplicator().run(ipl);
minIpl.setStack(Filters3D.filter(minIpl.getImageStack(),Filters3D.MIN,radiusXY,radiusXY,radiusZ));
for (int z = 1; z <= ipl.getNSlices(); z++) {
for (int c = 1; c <= ipl.getNChannels(); c++) {
for (int t = 1; t <= ipl.getNFrames(); t++) {
ipl.setPosition(c, z, t);
oriIpl.setPosition(c, z, t);
maxIpl.setPosition(c, z, t);
minIpl.setPosition(c, z, t);
byte[] pixels = (byte[]) ipl.getProcessor().getPixels();
float[] ori = (float[]) oriIpl.getProcessor().getPixels();
byte[] max = (byte[]) maxIpl.getProcessor().getPixels();
byte[] min = (byte[]) minIpl.getProcessor().getPixels();
for (int i = 0; i < pixels.length; i++) {
double val = Math.abs((max[i] & 0xff - pixels[i] & 0xff));
double thr = thrMult*Math.abs((pixels[i] & 0xff - min[i] & 0xff));
pixels[i] = (val <= thr & (ori[i]) > lowerThreshold) ? object : backg;
}
}
}
}
}
private void Mean(ImagePlus ipl, int radiusXY, int radiusZ, double thrMult, double par1, double par2, boolean doIwhite ) {
// See: Image Processing Learning Resourches HIPR2
// http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
ImagePlus meanIpl, oriIpl;
int c_value = 0;
byte object;
byte backg;
if (par1!=0) c_value= (int)par1;
if (doIwhite){
object = (byte) 0xff;
backg = (byte) 0;
}
else {
object = (byte) 0;
backg = (byte) 0xff;
}
oriIpl = new Duplicator().run(ipl);
IJ.run(oriIpl,"32-bit",null);
IntensityMinMax.run(ipl,true);
IJ.run(ipl,"8-bit",null);
// Normalising the image
meanIpl = new Duplicator().run(ipl);
// Converting to 32-bit and normalising
IJ.run(meanIpl,"32-bit",null);
// Applying 3D mean filter to meanIpl
meanIpl.setStack(Filters3D.filter(meanIpl.getImageStack(),Filters3D.MEAN,radiusXY,radiusXY,radiusZ));
for (int z = 1; z <= ipl.getNSlices(); z++) {
for (int c = 1; c <= ipl.getNChannels(); c++) {
for (int t = 1; t <= ipl.getNFrames(); t++) {
ipl.setPosition(c, z, t);
oriIpl.setPosition(c, z, t);
meanIpl.setPosition(c, z, t);
byte[] pixels = (byte[]) ipl.getProcessor().getPixels();
float[] ori = (float[]) oriIpl.getProcessor().getPixels();
float[] mean = (float[]) meanIpl.getProcessor().getPixels();
for (int i = 0; i < pixels.length; i++) {
double thr = thrMult * (mean[i] - c_value);
pixels[i] = ((pixels[i] & 0xff) > thr & (ori[i]) > lowerThreshold) ? object : backg;
}
}
}
}
ipl.setPosition(1,1,1);
}
private void Median(ImagePlus ipl, int radiusXY, int radiusZ, double thrMult, double par1, double par2, boolean doIwhite ) {
// See: Image Processing Learning Resourches HIPR2
// http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
ImagePlus medIpl, oriIpl;
int c_value = 0;
byte object;
byte backg;
if (par1!=0) c_value= (int) par1;
if (doIwhite){
object = (byte) 0xff;
backg = (byte) 0;
}
else {
object = (byte) 0;
backg = (byte) 0xff;
}
oriIpl = new Duplicator().run(ipl);
IJ.run(oriIpl,"32-bit",null);
IntensityMinMax.run(ipl,true);
IJ.run(ipl,"8-bit",null);
// Normalising the image
medIpl = new Duplicator().run(ipl);
// Converting to 32-bit and normalising
IJ.run(medIpl,"32-bit",null);
// Applying 3D mean filter to meanIpl
medIpl.setStack(Filters3D.filter(medIpl.getImageStack(),Filters3D.MEDIAN,radiusXY,radiusXY,radiusZ));
for (int z = 1; z <= ipl.getNSlices(); z++) {
for (int c = 1; c <= ipl.getNChannels(); c++) {
for (int t = 1; t <= ipl.getNFrames(); t++) {
ipl.setPosition(c, z, t);
oriIpl.setPosition(c, z, t);
medIpl.setPosition(c, z, t);
byte[] pixels = (byte[]) ipl.getProcessor().getPixels();
float[] ori = (float[]) oriIpl.getProcessor().getPixels();
float[] median = (float[]) medIpl.getProcessor().getPixels();
for (int i = 0; i < pixels.length; i++) {
double thr = thrMult*(median[i] - c_value);
pixels[i] = ((pixels[i] & 0xff) > thr & (ori[i]) > lowerThreshold) ? object : backg;
}
}
}
}
}
private void Phansalkar(ImagePlus ipl, int radiusXY, int radiusZ, double thrMult, double par1, double par2, boolean doIwhite) {
// Setting parameters (from Auto_Local_Threshold)
ImagePlus meanIpl, varIpl, normIpl, oriIpl;
double k_value = 0.25;
double r_value = 0.5;
double p_value = 2.0;
double q_value = 10.0;
byte object;
byte backg;
if (par1!=0) k_value= par1;
if (par2!=0) r_value= par2;
if (doIwhite){
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
oriIpl = new Duplicator().run(ipl);
IJ.run(oriIpl,"32-bit",null);
IntensityMinMax.run(ipl,true);
IJ.run(ipl,"8-bit",null);
// Normalising the image
normIpl = new Duplicator().run(ipl);
// Converting to 32-bit and normalising
IJ.run(normIpl,"32-bit",null);
ImageConverter ic = new ImageConverter(normIpl);
ic.convertToGray32();
for (int z = 1; z <= normIpl.getNSlices(); z++) {
for (int c = 1; c <= normIpl.getNChannels(); c++) {
for (int t = 1; t <= normIpl.getNFrames(); t++) {
normIpl.setPosition(c, z, t);
normIpl.getProcessor().multiply(1.0/255);
}
}
}
normIpl.setPosition(1,1,1);
// Duplicating oriIpl for calculation of mean and variance
meanIpl = new Duplicator().run(normIpl);
varIpl = new Duplicator().run(normIpl);
// Applying 3D mean filter to meanIpl
meanIpl.setStack(Filters3D.filter(meanIpl.getImageStack(),Filters3D.MEAN,radiusXY,radiusXY,radiusZ));
// Applying 3D variance filter to varIpl
varIpl.setStack(Filters3D.filter(varIpl.getImageStack(),Filters3D.VAR,radiusXY,radiusXY,radiusZ));
for (int z = 1; z <= ipl.getNSlices(); z++) {
for (int c = 1; c <= ipl.getNChannels(); c++) {
for (int t = 1; t <= ipl.getNFrames(); t++) {
ipl.setPosition(c, z, t);
oriIpl.setPosition(c, z, t);
normIpl.setPosition(c, z, t);
meanIpl.setPosition(c, z, t);
varIpl.setPosition(c, z, t);
byte[] pixels = (byte []) ipl.getProcessor().getPixels();
float[] ori = (float[]) oriIpl.getProcessor().getPixels();
float[] norm = (float []) normIpl.getProcessor().getPixels();
float[] mean = (float []) meanIpl.getProcessor().getPixels();
float[] var = (float []) varIpl.getProcessor().getPixels();
for (int i=0; i thr & (ori[i]) > lowerThreshold) ? object : backg;
}
}
}
}
ipl.setPosition(1,1,1);
}
@Override
public void run(String s) {
}
public double getLowerThreshold() {
return lowerThreshold;
}
public void setLowerThreshold(int lowerThreshold) {
this.lowerThreshold = lowerThreshold;
}
}
