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 setHistogram(ImagePlus imp, int j) {
ImageProcessor ip = imp.getProcessor();
ImageStatistics stats = ImageStatistics.getStatistics(ip, AREA + MODE, null);
int maxCount2 = 0;
histogram = stats.histogram;
for (int i = 0; i < stats.nBins; i++)
if ((histogram[i] > maxCount2) && (i != stats.mode)) maxCount2 = histogram[i];
hmax = stats.maxCount;
if ((hmax > (maxCount2 * 1.5)) && (maxCount2 != 0)) { // GL 1.5 was 2
hmax = (int) (maxCount2 * 1.1); // GL 1.1 was 1.5
histogram[stats.mode] = hmax;
}
os = null;
ColorModel cm = ip.getColorModel();
if (!(cm instanceof IndexColorModel)) return;
IndexColorModel icm = (IndexColorModel) cm;
int mapSize = icm.getMapSize();
if (mapSize != 256) return;
byte[] r = new byte[256];
byte[] g = new byte[256];
byte[] b = new byte[256];
icm.getReds(r);
icm.getGreens(g);
icm.getBlues(b);
hColors = new Color[256];
if (isRGB) {
if (j == 0) {
for (int i = 0; i < 256; i++) hColors[i] = new Color(i & 255, 0 & 255, 0 & 255);
} else if (j == 1) {
for (int i = 0; i < 256; i++) hColors[i] = new Color(0 & 255, i & 255, 0 & 255);
} else if (j == 2) {
for (int i = 0; i < 256; i++) hColors[i] = new Color(0 & 255, 0 & 255, i & 255);
}
} else {
if (j == 0) {
for (int i = 0; i < 256; i++) hColors[i] = new Color(r[i] & 255, g[i] & 255, b[i] & 255);
} else if (j == 1) {
for (int i = 0; i < 256; i++)
// hColors[i] = new Color(127-i/2&255, 127+i/2&255, 127-i/2&255);
hColors[i] = new Color(192 - i / 4 & 255, 192 + i / 4 & 255, 192 - i / 4 & 255);
} else if (j == 2) {
for (int i = 0; i < 256; i++) hColors[i] = new Color(i & 255, i & 255, 0 & 255);
}
}
}
boolean eraseOutsideRoi(ImageProcessor ip, Rectangle r, ImageProcessor mask) {
int width = ip.getWidth();
int height = ip.getHeight();
ip.setRoi(r);
if (excludeEdgeParticles && polygon != null) {
ImageStatistics stats = ImageStatistics.getStatistics(ip, MIN_MAX, null);
if (fillColor >= stats.min && fillColor <= stats.max) {
double replaceColor = level1 - 1.0;
if (replaceColor < 0.0 || replaceColor == fillColor) {
replaceColor = level2 + 1.0;
int maxColor = imageType == BYTE ? 255 : 65535;
if (replaceColor > maxColor || replaceColor == fillColor) {
IJ.error("Particle Analyzer", "Unable to remove edge particles");
return false;
}
}
for (int y = minY; y < maxY; y++) {
for (int x = minX; x < maxX; x++) {
int v = ip.getPixel(x, y);
if (v == fillColor) ip.putPixel(x, y, (int) replaceColor);
}
}
}
}
ip.setValue(fillColor);
if (mask != null) {
mask = mask.duplicate();
mask.invert();
ip.fill(mask);
}
ip.setRoi(0, 0, r.x, height);
ip.fill();
ip.setRoi(r.x, 0, r.width, r.y);
ip.fill();
ip.setRoi(r.x, r.y + r.height, r.width, height - (r.y + r.height));
ip.fill();
ip.setRoi(r.x + r.width, 0, width - (r.x + r.width), height);
ip.fill();
ip.resetRoi();
// IJ.log("erase: "+fillColor+" "+level1+" "+level2+" "+excludeEdgeParticles);
// (new ImagePlus("ip2", ip.duplicate())).show();
return true;
}
/* Method takes roi and substract its mean value from each frame in the image*/
public static ImagePlus DarkNoiseRemoval(ImagePlus imp_in, Roi noise_roi) {
ImagePlus imp_out = imp_in.duplicate();
int size = imp_in.getImageStackSize();
ImageProcessor ip = imp_out.getProcessor();
double minThreshold = ip.getMinThreshold();
double maxThreshold = ip.getMaxThreshold();
Calibration cal = imp_in.getCalibration();
int measurements = Analyzer.getMeasurements();
int current = imp_in.getCurrentSlice();
ImageStack stack = imp_in.getStack();
for (int i = 1; i <= size; i++) {
ip = stack.getProcessor(i);
if (minThreshold != ImageProcessor.NO_THRESHOLD)
ip.setThreshold(minThreshold, maxThreshold, ImageProcessor.NO_LUT_UPDATE);
ip.setRoi(noise_roi);
ImageStatistics stats = ImageStatistics.getStatistics(ip, measurements, cal);
ip.resetRoi();
ip.subtract(stats.mean);
stack.setProcessor(ip, i);
}
imp_out.setStack(stack);
return imp_out;
}
/*
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));
}
// returns value of mean intensity+3*SD based on
// fitting of image histogram to gaussian function
int getThreshold(ImageProcessor thImage) {
ImageStatistics imgstat;
double[][] dHistogram;
double[] dHistCum;
double[][] dNoiseFit;
int nHistSize;
int nCount, nMaxCount;
int nDownCount, nUpCount;
int i, nPeakPos;
double dRightWidth, dLeftWidth, dWidth;
double dMean, dSD;
double[] dFitErrors;
double dErrCoeff;
double dSum = 0.0;
double dSumFit = 0.0;
LMA fitlma;
// mean, sd, min, max
// imgstat = ImageStatistics.getStatistics(thImage, 38, null);
imgstat =
ImageStatistics.getStatistics(
thImage, Measurements.MEAN + Measurements.STD_DEV + Measurements.MIN_MAX, null);
dMean = imgstat.mean;
nPeakPos = 0;
nHistSize = imgstat.histogram.length;
dHistogram = new double[2][nHistSize];
nMaxCount = 0;
// determine position and height of maximum count in histogram (mode)
// and height at maximum
for (i = 0; i < nHistSize; i++) {
nCount = imgstat.histogram[i];
dHistogram[0][i] = imgstat.min + i * imgstat.binSize;
dHistogram[1][i] = (double) nCount;
dSum += (double) nCount;
if (nMaxCount < nCount) {
nMaxCount = nCount;
dMean = imgstat.min + i * imgstat.binSize;
nPeakPos = i;
}
}
// estimating width of a peak
// going to the left
i = nPeakPos;
while (i > 0 && imgstat.histogram[i] > 0.5 * nMaxCount) {
i--;
}
if (i < 0) i = 0;
dLeftWidth = i;
// going to the right
i = nPeakPos;
while (i < nHistSize && imgstat.histogram[i] > 0.5 * nMaxCount) {
i++;
}
if (i == nHistSize) i = nHistSize - 1;
dRightWidth = i;
// FWHM in bins
dWidth = (dRightWidth - dLeftWidth);
dSD = dWidth * imgstat.binSize / 2.35;
// fitting range +/- 3*SD
dLeftWidth = nPeakPos - 3 * dWidth / 2.35;
if (dLeftWidth < 0) dLeftWidth = 0;
dRightWidth = nPeakPos + 3 * dWidth / 2.35;
if (dRightWidth > nHistSize) dRightWidth = nHistSize;
nUpCount = (int) dRightWidth;
nDownCount = (int) dLeftWidth;
// preparing histogram range for fitting
dNoiseFit = new double[2][nUpCount - nDownCount + 1];
for (i = nDownCount; i <= nUpCount; i++) {
dNoiseFit[0][i - nDownCount] = dHistogram[0][i];
dNoiseFit[1][i - nDownCount] = dHistogram[1][i];
dSumFit += dHistogram[1][i];
}
// fitting range is too small, less than 80%
if ((dSumFit / dSum) < 0.8) {
// build cumulative distribution
dHistCum = new double[nHistSize];
dSumFit = 0;
for (i = 0; i < nHistSize; i++) {
dSumFit += dHistogram[1][i];
dHistCum[i] = dSumFit / dSum;
}
nUpCount = 0;
nDownCount = 0;
for (i = 0; i < nHistSize; i++) {
// 10% quantile
if (dHistCum[i] < 0.11) nDownCount = i;
// 90% quantile
if (dHistCum[i] < 0.91) nUpCount = i;
}
// preparing histogram range for fitting
dNoiseFit = new double[2][nUpCount - nDownCount + 1];
for (i = nDownCount; i <= nUpCount; i++) {
dNoiseFit[0][i - nDownCount] = dHistogram[0][i];
dNoiseFit[1][i - nDownCount] = dHistogram[1][i];
}
}
fitlma =
new LMA(new SMLOneDGaussian(), new double[] {(double) nMaxCount, dMean, dSD}, dNoiseFit);
fitlma.fit();
dMean = fitlma.parameters[1];
dSD = fitlma.parameters[2];
dFitErrors = fitlma.getStandardErrorsOfParameters();
// scaling coefficient for parameters errors estimation
// (Standard deviation of residuals)
dErrCoeff = Math.sqrt(fitlma.chi2 / (nUpCount - nDownCount + 1 - 3));
for (i = 0; i < 3; i++) dFitErrors[i] *= dErrCoeff;
for (i = 0; i < 3; i++) dFitErrors[i] *= 100 / fitlma.parameters[i];
if (dFitErrors[1] > 20
|| dMean < imgstat.min
|| dMean > imgstat.max
|| dSD < imgstat.min
|| dSD > imgstat.max)
// fit somehow failed
return (int) (imgstat.mean + 3.0 * imgstat.stdDev);
else return (int) (dMean + 3.0 * dSD);
}
void drawEllipse(ImageProcessor ip, ImageStatistics stats, int count) {
stats.drawEllipse(ip);
}
void analyzeParticle(int x, int y, ImagePlus imp, ImageProcessor ip) {
// Wand wand = new Wand(ip);
ImageProcessor ip2 = redirectIP != null ? redirectIP : ip;
wand.autoOutline(x, y, level1, level2, wandMode);
if (wand.npoints == 0) {
IJ.log("wand error: " + x + " " + y);
return;
}
Roi roi = new PolygonRoi(wand.xpoints, wand.ypoints, wand.npoints, roiType);
Rectangle r = roi.getBounds();
if (r.width > 1 && r.height > 1) {
PolygonRoi proi = (PolygonRoi) roi;
pf.setPolygon(proi.getXCoordinates(), proi.getYCoordinates(), proi.getNCoordinates());
ip2.setMask(pf.getMask(r.width, r.height));
if (floodFill) ff.particleAnalyzerFill(x, y, level1, level2, ip2.getMask(), r);
}
ip2.setRoi(r);
ip.setValue(fillColor);
ImageStatistics stats = getStatistics(ip2, measurements, calibration);
boolean include = true;
if (excludeEdgeParticles) {
if (r.x == minX || r.y == minY || r.x + r.width == maxX || r.y + r.height == maxY)
include = false;
if (polygon != null) {
Rectangle bounds = roi.getBounds();
int x1 = bounds.x + wand.xpoints[wand.npoints - 1];
int y1 = bounds.y + wand.ypoints[wand.npoints - 1];
int x2, y2;
for (int i = 0; i < wand.npoints; i++) {
x2 = bounds.x + wand.xpoints[i];
y2 = bounds.y + wand.ypoints[i];
if (!polygon.contains(x2, y2)) {
include = false;
break;
}
if ((x1 == x2 && ip.getPixel(x1, y1 - 1) == fillColor)
|| (y1 == y2 && ip.getPixel(x1 - 1, y1) == fillColor)) {
include = false;
break;
}
x1 = x2;
y1 = y2;
}
}
}
ImageProcessor mask = ip2.getMask();
if (minCircularity > 0.0 || maxCircularity < 1.0) {
double perimeter = roi.getLength();
double circularity =
perimeter == 0.0 ? 0.0 : 4.0 * Math.PI * (stats.pixelCount / (perimeter * perimeter));
if (circularity > 1.0) circularity = 1.0;
// IJ.log(circularity+" "+perimeter+" "+stats.area);
if (circularity < minCircularity || circularity > maxCircularity) include = false;
}
if (stats.pixelCount >= minSize && stats.pixelCount <= maxSize && include) {
particleCount++;
if (roiNeedsImage) roi.setImage(imp);
stats.xstart = x;
stats.ystart = y;
saveResults(stats, roi);
if (showChoice != NOTHING) drawParticle(drawIP, roi, stats, mask);
}
if (redirectIP != null) ip.setRoi(r);
ip.fill(mask);
}
/**
* Performs particle analysis on the specified ImagePlus and ImageProcessor. Returns false if
* there is an error.
*/
public boolean analyze(ImagePlus imp, ImageProcessor ip) {
if (this.imp == null) this.imp = imp;
showResults = (options & SHOW_RESULTS) != 0;
excludeEdgeParticles = (options & EXCLUDE_EDGE_PARTICLES) != 0;
resetCounter = (options & CLEAR_WORKSHEET) != 0;
showProgress = (options & SHOW_PROGRESS) != 0;
floodFill = (options & INCLUDE_HOLES) == 0;
recordStarts = (options & RECORD_STARTS) != 0;
addToManager = (options & ADD_TO_MANAGER) != 0;
displaySummary = (options & DISPLAY_SUMMARY) != 0;
inSituShow = (options & IN_SITU_SHOW) != 0;
outputImage = null;
ip.snapshot();
ip.setProgressBar(null);
if (Analyzer.isRedirectImage()) {
redirectImp = Analyzer.getRedirectImage(imp);
if (redirectImp == null) return false;
int depth = redirectImp.getStackSize();
if (depth > 1 && depth == imp.getStackSize()) {
ImageStack redirectStack = redirectImp.getStack();
redirectIP = redirectStack.getProcessor(imp.getCurrentSlice());
} else redirectIP = redirectImp.getProcessor();
} else if (imp.getType() == ImagePlus.COLOR_RGB) {
ImagePlus original = (ImagePlus) imp.getProperty("OriginalImage");
if (original != null
&& original.getWidth() == imp.getWidth()
&& original.getHeight() == imp.getHeight()) {
redirectImp = original;
redirectIP = original.getProcessor();
}
}
if (!setThresholdLevels(imp, ip)) return false;
width = ip.getWidth();
height = ip.getHeight();
if (!(showChoice == NOTHING || showChoice == OVERLAY_OUTLINES || showChoice == OVERLAY_MASKS)) {
blackBackground = Prefs.blackBackground && inSituShow;
if (slice == 1) outlines = new ImageStack(width, height);
if (showChoice == ROI_MASKS) drawIP = new ShortProcessor(width, height);
else drawIP = new ByteProcessor(width, height);
drawIP.setLineWidth(lineWidth);
if (showChoice == ROI_MASKS) {
} // Place holder for now...
else if (showChoice == MASKS && !blackBackground) drawIP.invertLut();
else if (showChoice == OUTLINES) {
if (!inSituShow) {
if (customLut == null) makeCustomLut();
drawIP.setColorModel(customLut);
}
drawIP.setFont(new Font("SansSerif", Font.PLAIN, fontSize));
if (fontSize > 12 && inSituShow) drawIP.setAntialiasedText(true);
}
outlines.addSlice(null, drawIP);
if (showChoice == ROI_MASKS || blackBackground) {
drawIP.setColor(Color.black);
drawIP.fill();
drawIP.setColor(Color.white);
} else {
drawIP.setColor(Color.white);
drawIP.fill();
drawIP.setColor(Color.black);
}
}
calibration = redirectImp != null ? redirectImp.getCalibration() : imp.getCalibration();
if (rt == null) {
rt = Analyzer.getResultsTable();
analyzer = new Analyzer(imp);
} else analyzer = new Analyzer(imp, measurements, rt);
if (resetCounter && slice == 1) {
if (!Analyzer.resetCounter()) return false;
}
beginningCount = Analyzer.getCounter();
byte[] pixels = null;
if (ip instanceof ByteProcessor) pixels = (byte[]) ip.getPixels();
if (r == null) {
r = ip.getRoi();
mask = ip.getMask();
if (displaySummary) {
if (mask != null) totalArea = ImageStatistics.getStatistics(ip, AREA, calibration).area;
else totalArea = r.width * calibration.pixelWidth * r.height * calibration.pixelHeight;
}
}
minX = r.x;
maxX = r.x + r.width;
minY = r.y;
maxY = r.y + r.height;
if (r.width < width || r.height < height || mask != null) {
if (!eraseOutsideRoi(ip, r, mask)) return false;
}
int offset;
double value;
int inc = Math.max(r.height / 25, 1);
int mi = 0;
ImageWindow win = imp.getWindow();
if (win != null) win.running = true;
if (measurements == 0) measurements = Analyzer.getMeasurements();
if (showChoice == ELLIPSES) measurements |= ELLIPSE;
measurements &= ~LIMIT; // ignore "Limit to Threshold"
roiNeedsImage =
(measurements & PERIMETER) != 0
|| (measurements & SHAPE_DESCRIPTORS) != 0
|| (measurements & FERET) != 0;
particleCount = 0;
wand = new Wand(ip);
pf = new PolygonFiller();
if (floodFill) {
ImageProcessor ipf = ip.duplicate();
ipf.setValue(fillColor);
ff = new FloodFiller(ipf);
}
roiType = Wand.allPoints() ? Roi.FREEROI : Roi.TRACED_ROI;
for (int y = r.y; y < (r.y + r.height); y++) {
offset = y * width;
for (int x = r.x; x < (r.x + r.width); x++) {
if (pixels != null) value = pixels[offset + x] & 255;
else if (imageType == SHORT) value = ip.getPixel(x, y);
else value = ip.getPixelValue(x, y);
if (value >= level1 && value <= level2) analyzeParticle(x, y, imp, ip);
}
if (showProgress && ((y % inc) == 0)) IJ.showProgress((double) (y - r.y) / r.height);
if (win != null) canceled = !win.running;
if (canceled) {
Macro.abort();
break;
}
}
if (showProgress) IJ.showProgress(1.0);
if (showResults) rt.updateResults();
imp.killRoi();
ip.resetRoi();
ip.reset();
if (displaySummary && IJ.getInstance() != null) updateSliceSummary();
if (addToManager && roiManager != null) roiManager.setEditMode(imp, true);
maxParticleCount = (particleCount > maxParticleCount) ? particleCount : maxParticleCount;
totalCount += particleCount;
if (!canceled) showResults();
return true;
}
public void runStuff(
DimensionMap map,
TreeMap<DimensionMap, ROIPlus> maximaMap,
TreeMap<DimensionMap, String> segMap,
TreeMap<DimensionMap, String> maskMap,
TreeMap<DimensionMap, String> imageMap,
TreeMap<DimensionMap, Double> results,
Canceler canceler) {
// Get the Maxima
ROIPlus maxima = maximaMap.get(map);
// Make the mask image impMask
// ByteProcessor impMask = (ByteProcessor) (new
// ImagePlus(maskMap.get(map)).getProcessor().convertToByte(false));
// ByteProcessor impSeg = (ByteProcessor) (new
// ImagePlus(segMap.get(map)).getProcessor().convertToByte(false));
ByteProcessor impSeg = (ByteProcessor) (new ImagePlus(segMap.get(map))).getProcessor();
ByteProcessor impMask = (ByteProcessor) (new ImagePlus(maskMap.get(map))).getProcessor();
ByteBlitter blit = new ByteBlitter(impSeg);
blit.copyBits(impMask, 0, 0, Blitter.AND);
FloatProcessor impImage =
(FloatProcessor) (new ImagePlus(imageMap.get(map))).getProcessor().convertToFloat();
Wand wand = new Wand(impSeg);
Wand wand2 = new Wand(impMask);
Vector<Double> measurements;
for (IdPoint p : maxima.getPointList()) {
if (canceler.isCanceled()) {
return;
}
if (impSeg.getPixel(p.x, p.y)
== 255) // if we land on a cell that made it through thresholding
{
wand.autoOutline(p.x, p.y); // outline it
wand2.autoOutline(p.x, p.y);
boolean partOfCellClump =
!this.selectionsAreEqual(
wand,
wand2); // If the segmented and unsegmented masks do not agree on the roi, then this
// cell is part of a clump.
if (wand.npoints > 0) {
Roi roi =
new PolygonRoi(
wand.xpoints,
wand.ypoints,
wand.npoints,
Roi.POLYGON); // The roi helps for using getLength() (DON'T USE Roi.TRACED_ROI.,
// IT SCREWS UP THE Polygon OBJECTS!!!! Bug emailed to ImageJ
// folks)
Polygon poly =
new Polygon(
wand.xpoints,
wand.ypoints,
wand.npoints); // The polygon helps for using contains()
Rectangle r = roi.getBounds();
measurements = new Vector<Double>();
for (int i = r.x; i < r.x + r.width; i++) {
for (int j = r.y; j < r.y + r.height; j++) {
// innerBoundary
if (poly.contains(i, j) && impSeg.getPixelValue(i, j) == 255) {
measurements.add((double) impImage.getPixelValue(i, j));
// Logs.log("In - " + innerT, this);
}
}
}
impMask.setRoi(roi);
ImageStatistics stats =
ImageStatistics.getStatistics(
impMask,
ImageStatistics.AREA
& ImageStatistics.PERIMETER
& ImageStatistics.CIRCULARITY
& ImageStatistics.ELLIPSE,
null);
if (measurements.size() > 0) {
DimensionMap resultsMap = map.copy();
resultsMap.put("Id", "" + p.id);
resultsMap.put("Measurement", "X");
results.put(resultsMap.copy(), (double) p.x);
resultsMap.put("Measurement", "Y");
results.put(resultsMap.copy(), (double) p.y);
resultsMap.put("Measurement", "AREA");
results.put(resultsMap.copy(), stats.area);
resultsMap.put("Measurement", "PERIMETER");
results.put(resultsMap.copy(), roi.getLength());
resultsMap.put("Measurement", "CIRCULARITY");
results.put(
resultsMap.copy(), 4.0 * Math.PI * (stats.area / (Math.pow(roi.getLength(), 2))));
resultsMap.put("Measurement", "ELLIPSE MAJOR");
results.put(resultsMap.copy(), stats.major);
resultsMap.put("Measurement", "ELLIPSE MINOR");
results.put(resultsMap.copy(), stats.minor);
resultsMap.put("Measurement", "MEAN");
results.put(resultsMap.copy(), StatisticsUtility.mean(measurements));
resultsMap.put("Measurement", "MEDIAN");
results.put(resultsMap.copy(), StatisticsUtility.median(measurements));
resultsMap.put("Measurement", "SUM");
results.put(resultsMap.copy(), StatisticsUtility.sum(measurements));
resultsMap.put("Measurement", "MIN");
results.put(resultsMap.copy(), StatisticsUtility.min(measurements));
resultsMap.put("Measurement", "MAX");
results.put(resultsMap.copy(), StatisticsUtility.max(measurements));
resultsMap.put("Measurement", "STDDEV");
results.put(resultsMap.copy(), StatisticsUtility.stdDev(measurements));
resultsMap.put("Measurement", "VARIANCE");
results.put(resultsMap.copy(), StatisticsUtility.variance(measurements));
resultsMap.put("Measurement", "CLUMP");
results.put(resultsMap.copy(), (double) (partOfCellClump ? 1 : 0));
}
}
}
}
}
