private void doRGBProjection(ImageStack stack) {
ImageStack[] channels = ChannelSplitter.splitRGB(stack, true);
ImagePlus red = new ImagePlus("Red", channels[0]);
ImagePlus green = new ImagePlus("Green", channels[1]);
ImagePlus blue = new ImagePlus("Blue", channels[2]);
imp.unlock();
ImagePlus saveImp = imp;
imp = red;
color = "(red)";
doProjection();
ImagePlus red2 = projImage;
imp = green;
color = "(green)";
doProjection();
ImagePlus green2 = projImage;
imp = blue;
color = "(blue)";
doProjection();
ImagePlus blue2 = projImage;
int w = red2.getWidth(), h = red2.getHeight(), d = red2.getStackSize();
if (method == SD_METHOD) {
ImageProcessor r = red2.getProcessor();
ImageProcessor g = green2.getProcessor();
ImageProcessor b = blue2.getProcessor();
double max = 0;
double rmax = r.getStatistics().max;
if (rmax > max) max = rmax;
double gmax = g.getStatistics().max;
if (gmax > max) max = gmax;
double bmax = b.getStatistics().max;
if (bmax > max) max = bmax;
double scale = 255 / max;
r.multiply(scale);
g.multiply(scale);
b.multiply(scale);
red2.setProcessor(r.convertToByte(false));
green2.setProcessor(g.convertToByte(false));
blue2.setProcessor(b.convertToByte(false));
}
RGBStackMerge merge = new RGBStackMerge();
ImageStack stack2 =
merge.mergeStacks(w, h, d, red2.getStack(), green2.getStack(), blue2.getStack(), true);
imp = saveImp;
projImage = new ImagePlus(makeTitle(), stack2);
}
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;
}
