void Bernsen(ImagePlus imp, int radius, 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 Maximp, Minimp;
ImageProcessor ip = imp.getProcessor(), ipMax, ipMin;
int contrast_threshold = 15;
int local_contrast;
int mid_gray;
byte object;
byte backg;
int temp;
if (par1 != 0) {
IJ.log("Bernsen: changed contrast_threshold from :" + contrast_threshold + " to:" + par1);
contrast_threshold = (int) par1;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Maximp = duplicateImage(ip);
ipMax = Maximp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMax, radius, rf.MAX); // Maximum
// Maximp.show();
Minimp = duplicateImage(ip);
ipMin = Minimp.getProcessor();
rf.rank(ipMin, radius, rf.MIN); // Minimum
// Minimp.show();
byte[] pixels = (byte[]) ip.getPixels();
byte[] max = (byte[]) ipMax.getPixels();
byte[] min = (byte[]) ipMin.getPixels();
for (int i = 0; i < pixels.length; i++) {
local_contrast = (int) ((max[i] & 0xff) - (min[i] & 0xff));
mid_gray = (int) ((min[i] & 0xff) + (max[i] & 0xff)) / 2;
temp = (int) (pixels[i] & 0x0000ff);
if (local_contrast < contrast_threshold)
pixels[i] = (mid_gray >= 128) ? object : backg; // Low contrast region
else pixels[i] = (temp >= mid_gray) ? object : backg;
}
// imp.updateAndDraw();
return;
}
void Contrast(ImagePlus imp, int radius, 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 Maximp, Minimp;
ImageProcessor ip = imp.getProcessor(), ipMax, ipMin;
int c_value = 0;
int mid_gray;
byte object;
byte backg;
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Maximp = duplicateImage(ip);
ipMax = Maximp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMax, radius, rf.MAX); // Maximum
// Maximp.show();
Minimp = duplicateImage(ip);
ipMin = Minimp.getProcessor();
rf.rank(ipMin, radius, rf.MIN); // Minimum
// Minimp.show();
byte[] pixels = (byte[]) ip.getPixels();
byte[] max = (byte[]) ipMax.getPixels();
byte[] min = (byte[]) ipMin.getPixels();
for (int i = 0; i < pixels.length; i++) {
pixels[i] =
((Math.abs((int) (max[i] & 0xff - pixels[i] & 0xff))
<= Math.abs((int) (pixels[i] & 0xff - min[i] & 0xff))))
? object
: backg;
}
// imp.updateAndDraw();
return;
}
void MidGrey(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// See: Image Processing Learning Resourches HIPR2
// http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
ImagePlus Maximp, Minimp;
ImageProcessor ip = imp.getProcessor(), ipMax, ipMin;
int c_value = 0;
int mid_gray;
byte object;
byte backg;
if (par1 != 0) {
IJ.log("MidGrey: changed c_value from :" + c_value + " to:" + par1);
c_value = (int) par1;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Maximp = duplicateImage(ip);
ipMax = Maximp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMax, radius, rf.MAX); // Maximum
// Maximp.show();
Minimp = duplicateImage(ip);
ipMin = Minimp.getProcessor();
rf.rank(ipMin, radius, rf.MIN); // Minimum
// Minimp.show();
byte[] pixels = (byte[]) ip.getPixels();
byte[] max = (byte[]) ipMax.getPixels();
byte[] min = (byte[]) ipMin.getPixels();
for (int i = 0; i < pixels.length; i++) {
pixels[i] =
((int) (pixels[i] & 0xff) > (int) (((max[i] & 0xff) + (min[i] & 0xff)) / 2) - c_value)
? object
: backg;
}
// imp.updateAndDraw();
return;
}
void Mean(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// See: Image Processing Learning Resourches HIPR2
// http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
ImagePlus Meanimp;
ImageProcessor ip = imp.getProcessor(), ipMean;
int c_value = 0;
byte object;
byte backg;
if (par1 != 0) {
IJ.log("Mean: changed c_value from :" + c_value + " to:" + par1);
c_value = (int) par1;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Meanimp = duplicateImage(ip);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] = ((int) (pixels[i] & 0xff) > (int) (mean[i] - c_value)) ? object : backg;
// imp.updateAndDraw();
return;
}
void Sauvola(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// Sauvola recommends K_VALUE = 0.5 and R_VALUE = 128.
// This is a modification of Niblack's thresholding method.
// Sauvola J. and Pietaksinen M. (2000) "Adaptive Document Image Binarization"
// Pattern Recognition, 33(2): 225-236
// http://www.ee.oulu.fi/mvg/publications/show_pdf.php?ID=24
// 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 Meanimp, Varimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
double k_value = 0.5;
double r_value = 128;
byte object;
byte backg;
if (par1 != 0) {
IJ.log("Sauvola: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log("Sauvola: 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);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] var = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((int) (pixels[i] & 0xff)
> (int) (mean[i] * (1.0 + k_value * ((Math.sqrt(var[i]) / r_value) - 1.0))))
? object
: backg;
// imp.updateAndDraw();
return;
}
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;
}
void Niblack(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// Niblack recommends K_VALUE = -0.2 for images with black foreground
// objects, and K_VALUE = +0.2 for images with white foreground objects.
// Niblack W. (1986) "An introduction to Digital Image Processing" Prentice-Hall.
// 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 Meanimp, Varimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
double k_value;
int c_value = 0;
byte object;
byte backg;
if (doIwhite) {
k_value = 0.2;
object = (byte) 0xff;
backg = (byte) 0;
} else {
k_value = -0.2;
object = (byte) 0;
backg = (byte) 0xff;
}
if (par1 != 0) {
IJ.log("Niblack: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log(
"Niblack: changed c_value from :"
+ c_value
+ " to:"
+ par2); // requested feature, not in original
c_value = (int) par2;
}
Meanimp = duplicateImage(ip);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] var = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((int) (pixels[i] & 0xff) > (int) (mean[i] + k_value * Math.sqrt(var[i]) - c_value))
? object
: backg;
// imp.updateAndDraw();
return;
}
