double rodbard(double x) {
// y = c*((a-x/(x-d))^(1/b)
// a=3.9, b=.88, c=712, d=44
double ex;
if (x == 0.0) ex = 5.0;
else ex = Math.exp(Math.log(x / 700.0) * 0.88);
double y = 3.9 - 44.0;
y = y / (1.0 + ex);
return y + 44.0;
}
public void run(String arg) {
int[] wList = WindowManager.getIDList();
if (wList == null) {
IJ.error("No images are open.");
return;
}
double thalf = 0.5;
boolean keep;
GenericDialog gd = new GenericDialog("Bleach correction");
gd.addNumericField("t½:", thalf, 1);
gd.addCheckbox("Keep source stack:", true);
gd.showDialog();
if (gd.wasCanceled()) return;
long start = System.currentTimeMillis();
thalf = gd.getNextNumber();
keep = gd.getNextBoolean();
if (keep) IJ.run("Duplicate...", "title='Bleach corrected' duplicate");
ImagePlus imp1 = WindowManager.getCurrentImage();
int d1 = imp1.getStackSize();
double v1, v2;
int width = imp1.getWidth();
int height = imp1.getHeight();
ImageProcessor ip1, ip2, ip3;
int slices = imp1.getStackSize();
ImageStack stack1 = imp1.getStack();
ImageStack stack2 = imp1.getStack();
int currentSlice = imp1.getCurrentSlice();
for (int n = 1; n <= slices; n++) {
ip1 = stack1.getProcessor(n);
ip3 = stack1.getProcessor(1);
ip2 = stack2.getProcessor(n);
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
v1 = ip1.getPixelValue(x, y);
v2 = ip3.getPixelValue(x, y);
// =B8/(EXP(-C$7*A8))
v1 = (v1 / Math.exp(-n * thalf));
ip2.putPixelValue(x, y, v1);
}
}
IJ.showProgress((double) n / slices);
IJ.showStatus(n + "/" + slices);
}
// stack2.show();
imp1.updateAndDraw();
}
void Phansalkar(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// This is a modification of Sauvola's thresholding method to deal with low contrast images.
// Phansalskar N. et al. Adaptive local thresholding for detection of nuclei in diversity
// stained
// cytology images.International Conference on Communications and Signal Processing (ICCSP),
// 2011,
// 218 - 220.
// In this method, the threshold t = mean*(1+p*exp(-q*mean)+k*((stdev/r)-1))
// Phansalkar recommends k = 0.25, r = 0.5, p = 2 and q = 10. In this plugin, k and r are the
// parameters 1 and 2 respectively, but the values of p and q are fixed.
//
// Implemented from Phansalkar's paper description by G. Landini
// This version uses a circular local window, instead of a rectagular one
ImagePlus Meanimp, Varimp, Orimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar, ipOri;
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) {
IJ.log("Phansalkar: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log("Phansalkar: changed r_value from :" + r_value + " to:" + par2);
r_value = par2;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Meanimp = duplicateImage(ip);
ContrastEnhancer ce = new ContrastEnhancer();
ce.stretchHistogram(Meanimp, 0.0);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
ipMean.multiply(1.0 / 255);
Orimp = duplicateImage(ip);
ce.stretchHistogram(Orimp, 0.0);
ic = new ImageConverter(Orimp);
ic.convertToGray32();
ipOri = Orimp.getProcessor();
ipOri.multiply(1.0 / 255); // original to compare
// Orimp.show();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ce.stretchHistogram(Varimp, 0.0);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
ipVar.multiply(1.0 / 255);
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
ipVar.sqr(); // SD
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] ori = (float[]) ipOri.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] sd = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((ori[i])
> (mean[i]
* (1.0
+ p_value * Math.exp(-q_value * mean[i])
+ k_value * ((sd[i] / r_value) - 1.0))))
? object
: backg;
// imp.updateAndDraw();
return;
}
