/** Generate output image whose type is same as input image. */
private ImagePlus makeOutputImage(ImagePlus imp, FloatProcessor fp, int ptype) {
int width = imp.getWidth();
int height = imp.getHeight();
float[] pixels = (float[]) fp.getPixels();
ImageProcessor oip = null;
// Create output image consistent w/ type of input image.
int size = pixels.length;
switch (ptype) {
case BYTE_TYPE:
oip = imp.getProcessor().createProcessor(width, height);
byte[] pixels8 = (byte[]) oip.getPixels();
for (int i = 0; i < size; i++) pixels8[i] = (byte) pixels[i];
break;
case SHORT_TYPE:
oip = imp.getProcessor().createProcessor(width, height);
short[] pixels16 = (short[]) oip.getPixels();
for (int i = 0; i < size; i++) pixels16[i] = (short) pixels[i];
break;
case FLOAT_TYPE:
oip = new FloatProcessor(width, height, pixels, null);
break;
}
// Adjust for display.
// Calling this on non-ByteProcessors ensures image
// processor is set up to correctly display image.
oip.resetMinAndMax();
// Create new image plus object. Don't use
// ImagePlus.createImagePlus here because there may be
// attributes of input image that are not appropriate for
// projection.
return new ImagePlus(makeTitle(), oip);
}
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 createMaskFromThreshold(ImagePlus imp) {
ImageProcessor ip = imp.getProcessor();
if (ip.getMinThreshold() == ImageProcessor.NO_THRESHOLD) {
IJ.error("Create Mask", "Area selection or thresholded image required");
return;
}
double t1 = ip.getMinThreshold();
double t2 = ip.getMaxThreshold();
IJ.run("Duplicate...", "title=mask");
ImagePlus imp2 = WindowManager.getCurrentImage();
ImageProcessor ip2 = imp2.getProcessor();
ip2.setThreshold(t1, t2, ImageProcessor.NO_LUT_UPDATE);
IJ.run("Convert to Mask");
}
void createEllipse(ImagePlus imp) {
IJ.showStatus("Fitting ellipse");
Roi roi = imp.getRoi();
if (roi == null) {
noRoi("Fit Ellipse");
return;
}
if (roi.isLine()) {
IJ.error("Fit Ellipse", "\"Fit Ellipse\" does not work with line selections");
return;
}
ImageProcessor ip = imp.getProcessor();
ip.setRoi(roi);
int options = Measurements.CENTROID + Measurements.ELLIPSE;
ImageStatistics stats = ImageStatistics.getStatistics(ip, options, null);
double dx = stats.major * Math.cos(stats.angle / 180.0 * Math.PI) / 2.0;
double dy = -stats.major * Math.sin(stats.angle / 180.0 * Math.PI) / 2.0;
double x1 = stats.xCentroid - dx;
double x2 = stats.xCentroid + dx;
double y1 = stats.yCentroid - dy;
double y2 = stats.yCentroid + dy;
double aspectRatio = stats.minor / stats.major;
imp.killRoi();
imp.setRoi(new EllipseRoi(x1, y1, x2, y2, aspectRatio));
}
void createMask(ImagePlus imp) {
Roi roi = imp.getRoi();
boolean useInvertingLut = Prefs.useInvertingLut;
Prefs.useInvertingLut = false;
if (roi == null || !(roi.isArea() || roi.getType() == Roi.POINT)) {
createMaskFromThreshold(imp);
Prefs.useInvertingLut = useInvertingLut;
return;
}
ImagePlus maskImp = null;
Frame frame = WindowManager.getFrame("Mask");
if (frame != null && (frame instanceof ImageWindow))
maskImp = ((ImageWindow) frame).getImagePlus();
if (maskImp == null) {
ImageProcessor ip = new ByteProcessor(imp.getWidth(), imp.getHeight());
if (!Prefs.blackBackground) ip.invertLut();
maskImp = new ImagePlus("Mask", ip);
maskImp.show();
}
ImageProcessor ip = maskImp.getProcessor();
ip.setRoi(roi);
ip.setValue(255);
ip.fill(ip.getMask());
maskImp.updateAndDraw();
Prefs.useInvertingLut = useInvertingLut;
}
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;
}
private ImagePlus duplicateImage(ImageProcessor iProcessor) {
int w = iProcessor.getWidth();
int h = iProcessor.getHeight();
ImagePlus iPlus = NewImage.createByteImage("Image", w, h, 1, NewImage.FILL_BLACK);
ImageProcessor imageProcessor = iPlus.getProcessor();
imageProcessor.copyBits(iProcessor, 0, 0, Blitter.COPY);
return iPlus;
}
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;
}
/**
* Execute the plugin functionality: duplicate and scale the given image.
*
* @return an Object[] array with the name and the scaled ImagePlus. Does NOT show the new, image;
* just returns it.
*/
public Object[] exec(
ImagePlus imp, String myMethod, int radius, double par1, double par2, boolean doIwhite) {
// 0 - Check validity of parameters
if (null == imp) return null;
ImageProcessor ip = imp.getProcessor();
int xe = ip.getWidth();
int ye = ip.getHeight();
// int [] data = (ip.getHistogram());
IJ.showStatus("Thresholding...");
long startTime = System.currentTimeMillis();
// 1 Do it
if (imp.getStackSize() == 1) {
ip.snapshot();
Undo.setup(Undo.FILTER, imp);
}
// Apply the selected algorithm
if (myMethod.equals("Bernsen")) {
Bernsen(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Contrast")) {
Contrast(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Mean")) {
Mean(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Median")) {
Median(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("MidGrey")) {
MidGrey(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Niblack")) {
Niblack(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Otsu")) {
Otsu(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Phansalkar")) {
Phansalkar(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Sauvola")) {
Sauvola(imp, radius, par1, par2, doIwhite);
}
// IJ.showProgress((double)(255-i)/255);
imp.updateAndDraw();
imp.getProcessor().setThreshold(255, 255, ImageProcessor.NO_LUT_UPDATE);
// 2 - Return the threshold and the image
IJ.showStatus("\nDone " + (System.currentTimeMillis() - startTime) / 1000.0);
return new Object[] {imp};
}
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 lineWidth() {
int width = (int) IJ.getNumber("Line Width:", Line.getWidth());
if (width == IJ.CANCELED) return;
Line.setWidth(width);
LineWidthAdjuster.update();
ImagePlus imp = WindowManager.getCurrentImage();
if (imp != null && imp.isProcessor()) {
ImageProcessor ip = imp.getProcessor();
ip.setLineWidth(Line.getWidth());
Roi roi = imp.getRoi();
if (roi != null && roi.isLine()) imp.draw();
}
}
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;
}
public void doHyperStackProjection(boolean allTimeFrames) {
int start = startSlice;
int stop = stopSlice;
int firstFrame = 1;
int lastFrame = imp.getNFrames();
if (!allTimeFrames) firstFrame = lastFrame = imp.getFrame();
ImageStack stack = new ImageStack(imp.getWidth(), imp.getHeight());
int channels = imp.getNChannels();
int slices = imp.getNSlices();
if (slices == 1) {
slices = imp.getNFrames();
firstFrame = lastFrame = 1;
}
int frames = lastFrame - firstFrame + 1;
increment = channels;
boolean rgb = imp.getBitDepth() == 24;
for (int frame = firstFrame; frame <= lastFrame; frame++) {
for (int channel = 1; channel <= channels; channel++) {
startSlice = (frame - 1) * channels * slices + (start - 1) * channels + channel;
stopSlice = (frame - 1) * channels * slices + (stop - 1) * channels + channel;
if (rgb) doHSRGBProjection(imp);
else doProjection();
stack.addSlice(null, projImage.getProcessor());
}
}
projImage = new ImagePlus(makeTitle(), stack);
projImage.setDimensions(channels, 1, frames);
if (channels > 1) {
projImage = new CompositeImage(projImage, 0);
((CompositeImage) projImage).copyLuts(imp);
if (method == SUM_METHOD || method == SD_METHOD)
((CompositeImage) projImage).resetDisplayRanges();
}
if (frames > 1) projImage.setOpenAsHyperStack(true);
Overlay overlay = imp.getOverlay();
if (overlay != null) {
startSlice = start;
stopSlice = stop;
if (imp.getType() == ImagePlus.COLOR_RGB)
projImage.setOverlay(projectRGBHyperStackRois(overlay));
else projImage.setOverlay(projectHyperStackRois(overlay));
}
IJ.showProgress(1, 1);
}
void createSelectionFromMask(ImagePlus imp) {
ImageProcessor ip = imp.getProcessor();
if (ip.getMinThreshold() != ImageProcessor.NO_THRESHOLD) {
IJ.runPlugIn("ij.plugin.filter.ThresholdToSelection", "");
return;
}
if (!ip.isBinary()) {
IJ.error(
"Create Selection",
"This command creates a composite selection from\n"
+ "a mask (8-bit binary image with white background)\n"
+ "or from an image that has been thresholded using\n"
+ "the Image>Adjust>Threshold tool. The current\n"
+ "image is not a mask and has not been thresholded.");
return;
}
int threshold = ip.isInvertedLut() ? 255 : 0;
ip.setThreshold(threshold, threshold, ImageProcessor.NO_LUT_UPDATE);
IJ.runPlugIn("ij.plugin.filter.ThresholdToSelection", "");
}
void createMask(ImagePlus imp) {
Roi roi = imp.getRoi();
boolean useInvertingLut = Prefs.useInvertingLut;
Prefs.useInvertingLut = false;
boolean selectAll =
roi != null
&& roi.getType() == Roi.RECTANGLE
&& roi.getBounds().width == imp.getWidth()
&& roi.getBounds().height == imp.getHeight()
&& imp.isThreshold();
if (roi == null || !(roi.isArea() || roi.getType() == Roi.POINT) || selectAll) {
createMaskFromThreshold(imp);
Prefs.useInvertingLut = useInvertingLut;
return;
}
ImagePlus maskImp = null;
Frame frame = WindowManager.getFrame("Mask");
if (frame != null && (frame instanceof ImageWindow))
maskImp = ((ImageWindow) frame).getImagePlus();
if (maskImp == null) {
ImageProcessor ip = new ByteProcessor(imp.getWidth(), imp.getHeight());
if (!Prefs.blackBackground) ip.invertLut();
maskImp = new ImagePlus("Mask", ip);
maskImp.show();
}
ImageProcessor ip = maskImp.getProcessor();
ip.setRoi(roi);
ip.setValue(255);
ip.fill(ip.getMask());
Calibration cal = imp.getCalibration();
if (cal.scaled()) {
Calibration cal2 = maskImp.getCalibration();
cal2.pixelWidth = cal.pixelWidth;
cal2.pixelHeight = cal.pixelHeight;
cal2.setUnit(cal.getUnit());
}
maskImp.updateAndRepaintWindow();
Prefs.useInvertingLut = useInvertingLut;
}
void Otsu(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// Otsu's threshold algorithm
// C++ code by Jordan Bevik <*****@*****.**>
// ported to ImageJ plugin by G.Landini. Same algorithm as in Auto_Threshold, this time on local
// circular regions
int[] data;
int w = imp.getWidth();
int h = imp.getHeight();
int position;
int radiusx2 = radius * 2;
ImageProcessor ip = imp.getProcessor();
byte[] pixels = (byte[]) ip.getPixels();
byte[] pixelsOut =
new byte
[pixels.length]; // need this to avoid changing the image data (and further histograms)
byte object;
byte backg;
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
int k, kStar; // k = the current threshold; kStar = optimal threshold
int N1, N; // N1 = # points with intensity <=k; N = total number of points
double BCV, BCVmax; // The current Between Class Variance and maximum BCV
double num, denom; // temporary bookeeping
int Sk; // The total intensity for all histogram points <=k
int S,
L =
256; // The total intensity of the image. Need to hange here if modifying for >8 bits
// images
int roiy;
Roi roi = new OvalRoi(0, 0, radiusx2, radiusx2);
// ip.setRoi(roi);
for (int y = 0; y < h; y++) {
IJ.showProgress(
(double) (y) / (h - 1)); // this method is slow, so let's show the progress bar
roiy = y - radius;
for (int x = 0; x < w; x++) {
roi.setLocation(x - radius, roiy);
ip.setRoi(roi);
// ip.setRoi(new OvalRoi(x-radius, roiy, radiusx2, radiusx2));
position = x + y * w;
data = ip.getHistogram();
// Initialize values:
S = N = 0;
for (k = 0; k < L; k++) {
S += k * data[k]; // Total histogram intensity
N += data[k]; // Total number of data points
}
Sk = 0;
N1 = data[0]; // The entry for zero intensity
BCV = 0;
BCVmax = 0;
kStar = 0;
// Look at each possible threshold value,
// calculate the between-class variance, and decide if it's a max
for (k = 1; k < L - 1; k++) { // No need to check endpoints k = 0 or k = L-1
Sk += k * data[k];
N1 += data[k];
// The float casting here is to avoid compiler warning about loss of precision and
// will prevent overflow in the case of large saturated images
denom = (double) (N1) * (N - N1); // Maximum value of denom is (N^2)/4 = approx. 3E10
if (denom != 0) {
// Float here is to avoid loss of precision when dividing
num = ((double) N1 / N) * S - Sk; // Maximum value of num = 255*N = approx 8E7
BCV = (num * num) / denom;
} else BCV = 0;
if (BCV >= BCVmax) { // Assign the best threshold found so far
BCVmax = BCV;
kStar = k;
}
}
// kStar += 1; // Use QTI convention that intensity -> 1 if intensity >= k
// (the algorithm was developed for I-> 1 if I <= k.)
// return kStar;
pixelsOut[position] = ((int) (pixels[position] & 0xff) > kStar) ? object : backg;
}
}
for (position = 0; position < w * h; position++)
pixels[position] = pixelsOut[position]; // update with thresholded pixels
}
public void run(String arg) {
imp = IJ.getImage();
int stackSize = imp.getStackSize();
if (imp == null) {
IJ.noImage();
return;
}
// Make sure input image is a stack.
if (stackSize == 1) {
IJ.error("Z Project", "Stack required");
return;
}
// Check for inverting LUT.
if (imp.getProcessor().isInvertedLut()) {
if (!IJ.showMessageWithCancel("ZProjection", lutMessage)) return;
}
// Set default bounds.
int channels = imp.getNChannels();
int frames = imp.getNFrames();
int slices = imp.getNSlices();
isHyperstack =
imp.isHyperStack()
|| (ij.macro.Interpreter.isBatchMode()
&& ((frames > 1 && frames < stackSize) || (slices > 1 && slices < stackSize)));
boolean simpleComposite = channels == stackSize;
if (simpleComposite) isHyperstack = false;
startSlice = 1;
if (isHyperstack) {
int nSlices = imp.getNSlices();
if (nSlices > 1) stopSlice = nSlices;
else stopSlice = imp.getNFrames();
} else stopSlice = stackSize;
// Build control dialog
GenericDialog gd = buildControlDialog(startSlice, stopSlice);
gd.showDialog();
if (gd.wasCanceled()) return;
if (!imp.lock()) return; // exit if in use
long tstart = System.currentTimeMillis();
setStartSlice((int) gd.getNextNumber());
setStopSlice((int) gd.getNextNumber());
method = gd.getNextChoiceIndex();
Prefs.set(METHOD_KEY, method);
if (isHyperstack) {
allTimeFrames = imp.getNFrames() > 1 && imp.getNSlices() > 1 ? gd.getNextBoolean() : false;
doHyperStackProjection(allTimeFrames);
} else if (imp.getType() == ImagePlus.COLOR_RGB) doRGBProjection(true);
else doProjection(true);
if (arg.equals("") && projImage != null) {
long tstop = System.currentTimeMillis();
projImage.setCalibration(imp.getCalibration());
if (simpleComposite) IJ.run(projImage, "Grays", "");
projImage.show("ZProjector: " + IJ.d2s((tstop - tstart) / 1000.0, 2) + " seconds");
}
imp.unlock();
IJ.register(ZProjector.class);
return;
}
/** Ask for parameters and then execute. */
public void run(String arg) {
// 1 - Obtain the currently active image:
ImagePlus imp = IJ.getImage();
if (null == imp) {
IJ.showMessage("There must be at least one image open");
return;
}
if (imp.getBitDepth() != 8) {
IJ.showMessage("Error", "Only 8-bit images are supported");
return;
}
// 2 - Ask for parameters:
GenericDialog gd = new GenericDialog("Auto Local Threshold");
String[] methods = {
"Try all",
"Bernsen",
"Contrast",
"Mean",
"Median",
"MidGrey",
"Niblack",
"Otsu",
"Phansalkar",
"Sauvola"
};
gd.addMessage("Auto Local Threshold v1.5");
gd.addChoice("Method", methods, methods[0]);
gd.addNumericField("Radius", 15, 0);
gd.addMessage("Special paramters (if different from default)");
gd.addNumericField("Parameter_1", 0, 0);
gd.addNumericField("Parameter_2", 0, 0);
gd.addCheckbox("White objects on black background", true);
if (imp.getStackSize() > 1) {
gd.addCheckbox("Stack", false);
}
gd.addMessage("Thresholded result is always shown in white [255].");
gd.showDialog();
if (gd.wasCanceled()) return;
// 3 - Retrieve parameters from the dialog
String myMethod = gd.getNextChoice();
int radius = (int) gd.getNextNumber();
double par1 = (double) gd.getNextNumber();
double par2 = (double) gd.getNextNumber();
boolean doIwhite = gd.getNextBoolean();
boolean doIstack = false;
int stackSize = imp.getStackSize();
if (stackSize > 1) doIstack = gd.getNextBoolean();
// 4 - Execute!
// long start = System.currentTimeMillis();
if (myMethod.equals("Try all")) {
ImageProcessor ip = imp.getProcessor();
int xe = ip.getWidth();
int ye = ip.getHeight();
int ml = methods.length;
ImagePlus imp2, imp3;
ImageStack tstack = null, stackNew;
if (stackSize > 1 && doIstack) {
boolean doItAnyway = true;
if (stackSize > 25) {
YesNoCancelDialog d =
new YesNoCancelDialog(
IJ.getInstance(),
"Auto Local Threshold",
"You might run out of memory.\n \nDisplay "
+ stackSize
+ " slices?\n \n \'No\' will process without display and\noutput results to the log window.");
if (!d.yesPressed()) {
// doIlog=true; //will show in the log window
doItAnyway = false;
}
if (d.cancelPressed()) return;
}
for (int j = 1; j <= stackSize; j++) {
imp.setSlice(j);
ip = imp.getProcessor();
tstack = new ImageStack(xe, ye);
for (int k = 1; k < ml; k++) tstack.addSlice(methods[k], ip.duplicate());
imp2 = new ImagePlus("Auto Threshold", tstack);
imp2.updateAndDraw();
for (int k = 1; k < ml; k++) {
imp2.setSlice(k);
Object[] result = exec(imp2, methods[k], radius, par1, par2, doIwhite);
}
// if (doItAnyway){
CanvasResizer cr = new CanvasResizer();
stackNew = cr.expandStack(tstack, (xe + 2), (ye + 18), 1, 1);
imp3 = new ImagePlus("Auto Threshold", stackNew);
imp3.updateAndDraw();
MontageMaker mm = new MontageMaker();
mm.makeMontage(imp3, 3, 3, 1.0, 1, (ml - 1), 1, 0, true); // 3 columns and 3 rows
}
imp.setSlice(1);
// if (doItAnyway)
IJ.run("Images to Stack", "method=[Copy (center)] title=Montage");
return;
} else { // single image try all
tstack = new ImageStack(xe, ye);
for (int k = 1; k < ml; k++) tstack.addSlice(methods[k], ip.duplicate());
imp2 = new ImagePlus("Auto Threshold", tstack);
imp2.updateAndDraw();
for (int k = 1; k < ml; k++) {
imp2.setSlice(k);
// IJ.log("analyzing slice with "+methods[k]);
Object[] result = exec(imp2, methods[k], radius, par1, par2, doIwhite);
}
// imp2.setSlice(1);
CanvasResizer cr = new CanvasResizer();
stackNew = cr.expandStack(tstack, (xe + 2), (ye + 18), 1, 1);
imp3 = new ImagePlus("Auto Threshold", stackNew);
imp3.updateAndDraw();
MontageMaker mm = new MontageMaker();
mm.makeMontage(imp3, 3, 3, 1.0, 1, (ml - 1), 1, 0, true);
return;
}
} else { // selected a method
if (stackSize > 1 && doIstack) { // whole stack
// if (doIstackHistogram) {// one global histogram
// Object[] result = exec(imp, myMethod, noWhite, noBlack, doIwhite, doIset, doIlog,
// doIstackHistogram );
// }
// else{ // slice by slice
for (int k = 1; k <= stackSize; k++) {
imp.setSlice(k);
Object[] result = exec(imp, myMethod, radius, par1, par2, doIwhite);
}
// }
imp.setSlice(1);
} else { // just one slice
Object[] result = exec(imp, myMethod, radius, par1, par2, doIwhite);
}
// 5 - If all went well, show the image:
// not needed here as the source image is binarised
}
}
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;
}
private void makeBand(ImagePlus imp) {
Roi roi = imp.getRoi();
if (roi == null) {
noRoi("Make Band");
return;
}
if (!roi.isArea()) {
IJ.error("Make Band", "Area selection required");
return;
}
Calibration cal = imp.getCalibration();
double pixels = bandSize;
double size = pixels * cal.pixelWidth;
int decimalPlaces = 0;
if ((int) size != size) decimalPlaces = 2;
GenericDialog gd = new GenericDialog("Make Band");
gd.addNumericField("Band Size:", size, decimalPlaces, 4, cal.getUnits());
gd.showDialog();
if (gd.wasCanceled()) return;
size = gd.getNextNumber();
if (Double.isNaN(size)) {
IJ.error("Make Band", "invalid number");
return;
}
int n = (int) Math.round(size / cal.pixelWidth);
if (n > 255) {
IJ.error("Make Band", "Cannot make bands wider that 255 pixels");
return;
}
int width = imp.getWidth();
int height = imp.getHeight();
Rectangle r = roi.getBounds();
ImageProcessor ip = roi.getMask();
if (ip == null) {
ip = new ByteProcessor(r.width, r.height);
ip.invert();
}
ImageProcessor mask = new ByteProcessor(width, height);
mask.insert(ip, r.x, r.y);
ImagePlus edm = new ImagePlus("mask", mask);
boolean saveBlackBackground = Prefs.blackBackground;
Prefs.blackBackground = false;
IJ.run(edm, "Distance Map", "");
Prefs.blackBackground = saveBlackBackground;
ip = edm.getProcessor();
ip.setThreshold(0, n, ImageProcessor.NO_LUT_UPDATE);
int xx = -1, yy = -1;
for (int x = r.x; x < r.x + r.width; x++) {
for (int y = r.y; y < r.y + r.height; y++) {
if (ip.getPixel(x, y) < n) {
xx = x;
yy = y;
break;
}
}
if (xx >= 0 || yy >= 0) break;
}
int count = IJ.doWand(edm, xx, yy, 0, null);
if (count <= 0) {
IJ.error("Make Band", "Unable to make band");
return;
}
ShapeRoi roi2 = new ShapeRoi(edm.getRoi());
if (!(roi instanceof ShapeRoi)) roi = new ShapeRoi(roi);
ShapeRoi roi1 = (ShapeRoi) roi;
roi2 = roi2.not(roi1);
imp.setRoi(roi2);
bandSize = n;
}
public void run(ImageProcessor ip) {
String[] imageNames = getOpenImageNames();
if (imageNames[0] == "None") {
IJ.error("need at least 2 binary open images");
return;
}
double previousMinOverlap = Prefs.get("BVTB.BinaryFeatureExtractor.minOverlap", 0);
boolean previousCombine = Prefs.get("BVTB.BinaryFeatureExtractor.combine", false);
GenericDialog gd = new GenericDialog("Binary Feature Extractor");
gd.addChoice("Objects image", imageNames, imageNames[0]);
gd.addChoice("Selector image", imageNames, imageNames[1]);
gd.addNumericField("Object_overlap in % (0=off)", previousMinOverlap, 0, 9, "");
gd.addCheckbox("Combine objects and selectors", previousCombine);
gd.addCheckbox("Count output", true);
gd.addCheckbox("Analysis tables", false);
gd.showDialog();
if (gd.wasCanceled()) {
return;
}
String objectsImgTitle = gd.getNextChoice();
String selectorsImgTitle = gd.getNextChoice();
double minOverlap = gd.getNextNumber();
boolean combineImages = gd.getNextBoolean();
boolean showCountOutput = gd.getNextBoolean();
boolean showAnalysis = gd.getNextBoolean();
if (gd.invalidNumber() || minOverlap < 0 || minOverlap > 100) {
IJ.error("invalid number");
return;
}
Prefs.set("BVTB.BinaryFeatureExtractor.minOverlap", minOverlap);
Prefs.set("BVTB.BinaryFeatureExtractor.combine", combineImages);
if (objectsImgTitle.equals(selectorsImgTitle)) {
IJ.error("images need to be different");
return;
}
ImagePlus objectsImp = WindowManager.getImage(objectsImgTitle);
ImageProcessor objectsIP = objectsImp.getProcessor();
ImagePlus selectorsImp = WindowManager.getImage(selectorsImgTitle);
ImageProcessor selectorsIP = selectorsImp.getProcessor();
if (!objectsIP.isBinary() || !selectorsIP.isBinary()) {
IJ.error("works with 8-bit binary images only");
return;
}
if ((objectsImp.getWidth() != selectorsImp.getWidth())
|| objectsImp.getHeight() != selectorsImp.getHeight()) {
IJ.error("images need to be of the same size");
return;
}
// close any existing RoiManager before instantiating a new one for this analysis
RoiManager oldRM = RoiManager.getInstance2();
if (oldRM != null) {
oldRM.close();
}
RoiManager objectsRM = new RoiManager(true);
ResultsTable objectsRT = new ResultsTable();
ParticleAnalyzer analyzeObjects =
new ParticleAnalyzer(analyzerOptions, measurementFlags, objectsRT, 0.0, 999999999.9);
analyzeObjects.setRoiManager(objectsRM);
analyzeObjects.analyze(objectsImp);
objectsRM.runCommand("Show None");
int objectNumber = objectsRT.getCounter();
Roi[] objectRoi = objectsRM.getRoisAsArray();
ResultsTable measureSelectorsRT = new ResultsTable();
Analyzer overlapAnalyzer = new Analyzer(selectorsImp, measurementFlags, measureSelectorsRT);
ImagePlus outputImp =
IJ.createImage("output", "8-bit black", objectsImp.getWidth(), objectsImp.getHeight(), 1);
ImageProcessor outputIP = outputImp.getProcessor();
double[] measuredOverlap = new double[objectNumber];
outputIP.setValue(255.0);
for (int o = 0; o < objectNumber; o++) {
selectorsImp.killRoi();
selectorsImp.setRoi(objectRoi[o]);
overlapAnalyzer.measure();
measuredOverlap[o] = measureSelectorsRT.getValue("%Area", o);
if (minOverlap != 0.0 && measuredOverlap[o] >= minOverlap) {
outputIP.fill(objectRoi[o]);
finalCount++;
} else if (minOverlap == 0.0 && measuredOverlap[o] > 0.0) {
outputIP.fill(objectRoi[o]);
finalCount++;
}
}
// measureSelectorsRT.show("Objects");
selectorsImp.killRoi();
RoiManager selectorRM = new RoiManager(true);
ResultsTable selectorRT = new ResultsTable();
ParticleAnalyzer.setRoiManager(selectorRM);
ParticleAnalyzer analyzeSelectors =
new ParticleAnalyzer(analyzerOptions, measurementFlags, selectorRT, 0.0, 999999999.9);
analyzeSelectors.analyze(selectorsImp);
selectorRM.runCommand("Show None");
int selectorNumber = selectorRT.getCounter();
if (combineImages) {
outputImp.updateAndDraw();
Roi[] selectorRoi = selectorRM.getRoisAsArray();
ResultsTable measureObjectsRT = new ResultsTable();
Analyzer selectorAnalyzer = new Analyzer(outputImp, measurementFlags, measureObjectsRT);
double[] selectorOverlap = new double[selectorNumber];
outputIP.setValue(255.0);
for (int s = 0; s < selectorNumber; s++) {
outputImp.killRoi();
outputImp.setRoi(selectorRoi[s]);
selectorAnalyzer.measure();
selectorOverlap[s] = measureObjectsRT.getValue("%Area", s);
if (selectorOverlap[s] > 0.0d) {
outputIP.fill(selectorRoi[s]);
}
}
selectorRoi = null;
selectorAnalyzer = null;
measureObjectsRT = null;
}
// selectorRT.show("Selectors");
outputImp.killRoi();
String outputImageTitle = WindowManager.getUniqueName("Extracted_" + objectsImgTitle);
outputImp.setTitle(outputImageTitle);
outputImp.show();
outputImp.changes = true;
if (showCountOutput) {
String[] openTextWindows = WindowManager.getNonImageTitles();
boolean makeNewTable = true;
for (int w = 0; w < openTextWindows.length; w++) {
if (openTextWindows[w].equals("BFE_Results")) {
makeNewTable = false;
}
}
TextWindow existingCountTable = ResultsTable.getResultsWindow();
if (makeNewTable) {
countTable = new ResultsTable();
countTable.setPrecision(0);
countTable.setValue("Image", 0, outputImageTitle);
countTable.setValue("Objects", 0, objectNumber);
countTable.setValue("Selectors", 0, selectorNumber);
countTable.setValue("Extracted", 0, finalCount);
countTable.show("BFE_Results");
} else {
IJ.renameResults("BFE_Results", "Results");
countTable = ResultsTable.getResultsTable();
countTable.setPrecision(0);
countTable.incrementCounter();
countTable.addValue("Image", outputImageTitle);
countTable.addValue("Objects", objectNumber);
countTable.addValue("Selectors", selectorNumber);
countTable.addValue("Extracted", finalCount);
IJ.renameResults("Results", "BFE_Results");
countTable.show("BFE_Results");
}
}
if (showAnalysis) {
ResultsTable extractedRT = new ResultsTable();
ParticleAnalyzer analyzeExtracted =
new ParticleAnalyzer(
ParticleAnalyzer.CLEAR_WORKSHEET | ParticleAnalyzer.RECORD_STARTS,
measurementFlags,
extractedRT,
0.0,
999999999.9);
analyzeExtracted.analyze(outputImp);
objectsRT.show("Objects");
selectorRT.show("Selectors");
extractedRT.show("Extracted");
} else {
objectsRT = null;
selectorRT = null;
}
objectsRM = null;
measureSelectorsRT = null;
analyzeObjects = null;
overlapAnalyzer = null;
objectRoi = null;
selectorRM = null;
objectsImp.killRoi();
objectsImp.changes = false;
selectorsImp.changes = false;
}
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;
}
/*
if selection is closed shape, create a circle with the same area and centroid, otherwise use<br>
the Pratt method to fit a circle to the points that define the line or multi-point selection.<br>
Reference: Pratt V., Direct least-squares fitting of algebraic surfaces", Computer Graphics, Vol. 21, pages 145-152 (1987).<br>
Original code: Nikolai Chernov's MATLAB script for Newton-based Pratt fit.<br>
(http://www.math.uab.edu/~chernov/cl/MATLABcircle.html)<br>
Java version: https://github.com/mdoube/BoneJ/blob/master/src/org/doube/geometry/FitCircle.java<br>
@authors Nikolai Chernov, Michael Doube, Ved Sharma
*/
void fitCircle(ImagePlus imp) {
Roi roi = imp.getRoi();
if (roi == null) {
noRoi("Fit Circle");
return;
}
if (roi.isArea()) { // create circle with the same area and centroid
ImageProcessor ip = imp.getProcessor();
ip.setRoi(roi);
ImageStatistics stats =
ImageStatistics.getStatistics(ip, Measurements.AREA + Measurements.CENTROID, null);
double r = Math.sqrt(stats.pixelCount / Math.PI);
imp.killRoi();
int d = (int) Math.round(2.0 * r);
IJ.makeOval(
(int) Math.round(stats.xCentroid - r), (int) Math.round(stats.yCentroid - r), d, d);
return;
}
Polygon poly = roi.getPolygon();
int n = poly.npoints;
int[] x = poly.xpoints;
int[] y = poly.ypoints;
if (n < 3) {
IJ.error("Fit Circle", "At least 3 points are required to fit a circle.");
return;
}
// calculate point centroid
double sumx = 0, sumy = 0;
for (int i = 0; i < n; i++) {
sumx = sumx + poly.xpoints[i];
sumy = sumy + poly.ypoints[i];
}
double meanx = sumx / n;
double meany = sumy / n;
// calculate moments
double[] X = new double[n], Y = new double[n];
double Mxx = 0, Myy = 0, Mxy = 0, Mxz = 0, Myz = 0, Mzz = 0;
for (int i = 0; i < n; i++) {
X[i] = x[i] - meanx;
Y[i] = y[i] - meany;
double Zi = X[i] * X[i] + Y[i] * Y[i];
Mxy = Mxy + X[i] * Y[i];
Mxx = Mxx + X[i] * X[i];
Myy = Myy + Y[i] * Y[i];
Mxz = Mxz + X[i] * Zi;
Myz = Myz + Y[i] * Zi;
Mzz = Mzz + Zi * Zi;
}
Mxx = Mxx / n;
Myy = Myy / n;
Mxy = Mxy / n;
Mxz = Mxz / n;
Myz = Myz / n;
Mzz = Mzz / n;
// calculate the coefficients of the characteristic polynomial
double Mz = Mxx + Myy;
double Cov_xy = Mxx * Myy - Mxy * Mxy;
double Mxz2 = Mxz * Mxz;
double Myz2 = Myz * Myz;
double A2 = 4 * Cov_xy - 3 * Mz * Mz - Mzz;
double A1 = Mzz * Mz + 4 * Cov_xy * Mz - Mxz2 - Myz2 - Mz * Mz * Mz;
double A0 = Mxz2 * Myy + Myz2 * Mxx - Mzz * Cov_xy - 2 * Mxz * Myz * Mxy + Mz * Mz * Cov_xy;
double A22 = A2 + A2;
double epsilon = 1e-12;
double ynew = 1e+20;
int IterMax = 20;
double xnew = 0;
int iterations = 0;
// Newton's method starting at x=0
for (int iter = 1; iter <= IterMax; iter++) {
iterations = iter;
double yold = ynew;
ynew = A0 + xnew * (A1 + xnew * (A2 + 4. * xnew * xnew));
if (Math.abs(ynew) > Math.abs(yold)) {
if (IJ.debugMode) IJ.log("Fit Circle: wrong direction: |ynew| > |yold|");
xnew = 0;
break;
}
double Dy = A1 + xnew * (A22 + 16 * xnew * xnew);
double xold = xnew;
xnew = xold - ynew / Dy;
if (Math.abs((xnew - xold) / xnew) < epsilon) break;
if (iter >= IterMax) {
if (IJ.debugMode) IJ.log("Fit Circle: will not converge");
xnew = 0;
}
if (xnew < 0) {
if (IJ.debugMode) IJ.log("Fit Circle: negative root: x = " + xnew);
xnew = 0;
}
}
if (IJ.debugMode)
IJ.log("Fit Circle: n=" + n + ", xnew=" + IJ.d2s(xnew, 2) + ", iterations=" + iterations);
// calculate the circle parameters
double DET = xnew * xnew - xnew * Mz + Cov_xy;
double CenterX = (Mxz * (Myy - xnew) - Myz * Mxy) / (2 * DET);
double CenterY = (Myz * (Mxx - xnew) - Mxz * Mxy) / (2 * DET);
double radius = Math.sqrt(CenterX * CenterX + CenterY * CenterY + Mz + 2 * xnew);
if (Double.isNaN(radius)) {
IJ.error("Fit Circle", "Points are collinear.");
return;
}
CenterX = CenterX + meanx;
CenterY = CenterY + meany;
imp.killRoi();
IJ.makeOval(
(int) Math.round(CenterX - radius),
(int) Math.round(CenterY - radius),
(int) Math.round(2 * radius),
(int) Math.round(2 * radius));
}
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;
}
