コード例 #1
0
ファイル: Selection.java プロジェクト: dscho/ImageJA
 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;
 }
コード例 #2
0
ファイル: Bleach_.java プロジェクト: HelioScan/HelioScan
  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();
  }
コード例 #3
0
  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;
  }