def getFlipArray(maskPAImgPlus):
"""Given the PA-aligned mask image, return a list of booleans, indicating whether or not we should flip the corresponding index"""
maskPAImgPlus.show()
# IJ.run(maskPAImgPlus, "Shape Smoothing", "relative_proportion_fds=15 absolute_number_fds=2 keep=[Relative_proportion of FDs] stack");
IJ.run("Set Measurements...", "shape")
IJ.setThreshold(maskPAImgPlus, 1, 1000, "No Update")
stk = maskPAImgPlus.getStack()
tableLeft = ResultsTable()
PA.setResultsTable(tableLeft)
IJ.makeRectangle(0, 0,
maskPAImgPlus.getWidth() / 2 - 5,
maskPAImgPlus.getHeight())
IJ.run(maskPAImgPlus, "Analyze Particles...", "stack")
tableRight = ResultsTable()
PA.setResultsTable(tableRight)
IJ.makeRectangle(maskPAImgPlus.getWidth() / 2 - 5, 0,
maskPAImgPlus.getWidth() / 2 + 5,
maskPAImgPlus.getHeight())
IJ.run(maskPAImgPlus, "Analyze Particles...", "stack")
maskPAImgPlus.hide()
IJ.resetThreshold(maskPAImgPlus)
leftCirc = [tableLeft.getValue("Circ.", i) for i in range(stk.getSize())]
rightCirc = [tableRight.getValue("Circ.", i) for i in range(stk.getSize())]
return [leftCirc[i] < rightCirc[i] for i in range(len(leftCirc))]
def test():
newImg = ImagePlus("GrayScaled", imp)
newip = newImg.getProcessor()
hist = newip.getHistogram()
lowTH = Auto_Threshold.IsoData(hist)
newip.setThreshold(lowTH, max(hist), ImageProcessor.BLACK_AND_WHITE_LUT)
rt = ResultsTable()
pa = ParticleAnalyzer(ParticleAnalyzer.SHOW_RESULTS | ParticleAnalyzer.SHOW_OVERLAY_OUTLINES, Measurements.AREA |Measurements.MEAN |\
Measurements.MEDIAN | Measurements.STD_DEV | Measurements.MIN_MAX | Measurements.RECT, rt,50, 200000, 0.5, 1 )
pa.setResultsTable(rt)
pa.analyze(newImg)
rt.show("Results")
def test():
newImg = ImagePlus("GrayScaled",imp)
newip = newImg.getProcessor()
hist = newip.getHistogram()
lowTH = Auto_Threshold.IsoData(hist)
newip.setThreshold(lowTH, max(hist),ImageProcessor.BLACK_AND_WHITE_LUT)
rt = ResultsTable()
pa = ParticleAnalyzer(ParticleAnalyzer.SHOW_RESULTS | ParticleAnalyzer.SHOW_OVERLAY_OUTLINES, Measurements.AREA |Measurements.MEAN |\
Measurements.MEDIAN | Measurements.STD_DEV | Measurements.MIN_MAX | Measurements.RECT, rt,50, 200000, 0.5, 1 )
pa.setResultsTable(rt)
pa.analyze(newImg)
rt.show("Results")
def cellSegmentation(srcDir, dstDir, currentDir, filename, keepDirectories):
print "Processing:"
# Opening the image
print "Open image file", filename
imp = IJ.openImage(os.path.join(currentDir, dstDir))
# Put your processing commands here!
localinput=srcDir.replace("/", "\\")
saveDir = localinput.replace(srcDir, dstDir)
string="."
dotIndex=filename.find(string)
localfile= filename[0:dotIndex]
print(localfile)
IJ.run("New... ", "name="+f+" type=Table")
print(f,"\\Headings:Cell\tarea\tCirc\tAR\tRoundness\tMaximum")
IJ.run("Bio-Formats", "open=[" + localinput + os.path.sep + filename +"] autoscale color_mode=Default rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT")
IJ.open()
idd= WM.getIDList();
imageID= idd[0];
IJ.run("Clear Results")
WM.getImage(imageID)
IJ.run("Duplicate...", "duplicate channels="+str(x)+"") #Nucleus channel #took away x
IJ.run("Z Project...", "projection=[Standard Deviation]");#picture for frame detection
IJ.run("8-bit");
IJ.run("Duplicate...", "title=IMAGE");#frame
IJ.run("Duplicate...", "title=SUBTRACT");#Background subtraction mask (for frame and watershed)
imp=IJ.getImage()
pixelWidth=imp.getWidth()
pixelWidth=pixelWidth/1647.89
pixelHeight= imp.getHeight()
#create subtraction mask, applying constraining maximum (step I)
IJ.selectWindow("SUBTRACT")
nResults=imp.getStatistics()
row = nResults
rt_exist = WM.getWindow("Results")
if rt_exist==None:
rt= ResultsTable()
else:
rt = rt_exist.getTextPanel().getOrCreateResultsTable()
rt.setValue("Max ", 0, row.max) #text file
rt.show("Results")
u=math.floor(row.mean*3)
IJ.run("Max...","value="+str(u)) #constraining maximum of 3-fold mean to reduce effect of extreme values during subtraction
#gaussian blurring (step II)
IJ.run("Gaussian Blur...", "sigma=100 scaled") #blurring for subtraction mask
IJ.selectWindow("IMAGE")
pxrollrad = cellradius/pixelWidth; #rolling ball radius in pixels needed (= predefined cell radius[µm]/pixelsize[µm/px])
IJ.run("Subtract Background...", "rolling="+str(pxrollrad)+"")
IJ.run("Gaussian Blur...", "sigma=2 scaled") #reduces punctate character of grayscale image '
IM=IJ.selectWindow("IMAGE")
SUB=IJ.selectWindow("SUBTRACT")
ic().run("SUBTRACT", IM, SUB) #just subtracts two images
IJ.selectWindow("IMAGE") #see how to call
IJ.run("Duplicate...", "title=AND")#watershed
IJ.run("Duplicate...", "title=CHECK")#for checking if maxima exist within selection later
#Apply threshold to get binary image of cell borders (step IV)
IJ.selectWindow("IMAGE")
imp = IJ.getImage() # the current image
imp.getProcessor().setThreshold(1, 255, ImageProcessor.NO_LUT_UPDATE)
IJ.run("Subtract Background...","...")
IJ.run("Convert to Mask", "method=Default background=Dark only black")
IJ.run("Fill Holes")
#Create watershed line image (step V)
IJ.selectWindow("AND")
IJ.run("Gaussian Blur...", "sigma=2 scaled")
imp=IJ.getImage()
pixelWidth=imp.getWidth()
pixelWidth=pixelWidth/1647.89
pixelHeight= imp.getHeight()
# Saving the image
nResults=imp.getStatistics()
row = nResults
rt.setValue("Max ", 1, row.max) #text file
nBins = 256
Hist = HistogramWindow("Histogram",imp,nBins)
Table = Hist.getResultsTable()
Counts = Table.getColumn(1)
#mean gray value of pixels belonging to cells needed (i.e. mean of ONLY non-zero pixel)
Sum = 0 #all counts
CV = 0 #weighed counts (= counts * intensity)
for i in range(0, len(Counts)): #starting with 1 instead of 0. -> 0 intensity values are not considered.
Sum += Counts[i]
CV += Counts[i]*i
m = (CV/Sum)
m=math.floor(m)
l = math.floor(2*m) #Maxima need to be at least twice the intensity of cellular mean intensity
IJ.run("Find Maxima...", "noise="+str(l)+" output=[Segmented Particles] exclude") #watershedding
#Combine watershed lines and cell frame (step VI)
IJ.selectWindow("IMAGE")
imp=IJ.getImage()
imp.getProcessor().setThreshold(1, 255, ImageProcessor.NO_LUT_UPDATE)
IJ.run(imp, "Watershed", "") #useful
imp = IJ.getImage()
ip = imp.getProcessor()
segip = MaximumFinder().findMaxima( ip, 1, ImageProcessor.NO_THRESHOLD, MaximumFinder.SEGMENTED , False, False)
segip.invert()
segimp = ImagePlus("seg", segip)
segimp.show()
mergeimp = RGBStackMerge.mergeChannels(array([segimp, None, None, imp, None, None, None], ImagePlus), True)
mergeimp.show()
pa_exist = WM.getWindow("Results for PA")
if pa_exist==None:
pa_rt= ResultsTable()
else:
pa_rt = pa_exist.getTextPanel().getOrCreateResultsTable()
ParticleAnalyzer.setResultsTable(pa_rt)
IJ.run("Set Measurements...", "area mean perimeter shape decimal=3")
IJ.run("Analyze Particles...", "size=" + str(cellradius) + "-Infinity circularity=0.1-1.00 add"); #Cell bodies detected
pa_rt.show("Results for PA ")
save_all(srcDir, dstDir, filename, localfile, keepDirectories, imageID)
rotateStack(img470PA, angles, xs, ys, IP.BILINEAR)
rotateStack(maskPA, angles, xs, ys, IP.NONE)
flipIfNecessary(getFlipArray(maskPA), [img410PA, img470PA, maskPA])
img410PA.show()
img470PA.show()
# MORPHOLOGICAL MEASUREMENTS
# These are done on the rotated image mask
scales = {
"4x4": 2.58,
"2x2": 1.29
}
morphTable = ResultsTable()
PA.setResultsTable(morphTable)
maskPA.show()
IJ.run(maskPA, "Select None", "")
IJ.run(maskPA, "Set Scale...", "distance=1 known=" + str(scales[binning]) + " pixel=1 unit=μm"); #TODO scale?
IJ.run(maskPA, "Set Measurements...", "area centroid center perimeter bounding fit shape feret's median skewness kurtosis scientific redirect=None decimal=7");
IJ.run(maskPA, "Analyze Particles...", "size=0-Infinity circularity=0.00-1.00 show=Nothing stack");
morph_headings = morphTable.getColumnHeadings().strip(' ').split('\t')
for m_heading in morph_headings[1:]: # because of formatting, the first is empty
addValues(dataTable, m_heading, [morphTable.getValue(m_heading, i) for i in range(morphTable.size())])
for imPlus in [img410PA, img470PA, maskPA]:
cropStack(imPlus)
IJ.saveAsTiff(imPlus, os.path.join(PARENT_DIR, imPlus.getTitle()))