def analyze(imp, min_area):
MAXSIZE = 1000000000000
MINCIRCULARITY = 0.0
MAXCIRCULARITY = 1.
options = PA.SHOW_MASKS
temp_results = ResultsTable()
p = PA(options, PA.AREA + PA.MEAN, temp_results, min_area, MAXSIZE, MINCIRCULARITY, MAXCIRCULARITY)
p.setHideOutputImage(True)
p.analyze(imp)
if temp_results.getCounter() == 0:
areas = []
signals = []
else:
areas = list(temp_results.getColumn(0))
signals = list(temp_results.getColumn(1))
count = len(areas)
area = sum(areas)
total = 0
if area > 0:
total = sum([a*s for a,s in zip(areas, signals)]) / area
return p.getOutputImage(), count, area, total
def particleAnalysis(slicenum, sliceimp, resrt):
options = PA.SHOW_NONE
rt = ResultsTable()
p = PA(options, PA.AREA + PA.CENTROID, rt, MINSIZE, MAXSIZE)
p.analyze(sliceimp)
centrosomecounts = rt.getCounter()
print "-- Region", str(slicenum), "--"
curCount = resrt.getCounter()
resrt.setValue("cellID", curCount, slicenum)
resrt.setValue("counts", curCount, centrosomecounts)
if centrosomecounts == 0:
print " .. no centrosome"
elif centrosomecounts == 1:
print " .. Only one centrosome"
# if rt.getValue("Area", 0) == 1.0:
# print " .. Only one centrosome"
# else:
# print " .. one centrosome but Area 1 < : maybe combined"
elif centrosomecounts == 2:
print " .. two centrosomes"
resrt.setValue("c1x", curCount, rt.getValue("X", 0))
resrt.setValue("c1y", curCount, rt.getValue("Y", 0))
resrt.setValue("c2x", curCount, rt.getValue("X", 1))
resrt.setValue("c2y", curCount, rt.getValue("Y", 1))
else:
print " .. ", str(centrosomecounts),"centrosomes detected"
def keep_largest_blob(imp):
"""remove all blobs other than the largest by area"""
rt = ResultsTable()
mxsz = imp.width * imp.height
roim = RoiManager(False)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
ParticleAnalyzer.AREA | ParticleAnalyzer.SLICE, rt,
0, mxsz)
pa.setRoiManager(roim)
for idx in range(1, imp.getImageStackSize() + 1):
roim.reset()
rt.reset()
imp.setPosition(idx)
pa.analyze(imp)
rt_areas = rt.getColumn(rt.getColumnIndex("Area")).tolist()
mx_ind = rt_areas.index(max(rt_areas))
indices_to_remove = [
a for a in range(0, len(rt_areas)) if a != mx_ind
]
indices_to_remove.reverse()
for rem_idx in indices_to_remove:
roim.select(imp, rem_idx)
IJ.run(imp, "Set...", "value=0 slice")
imp.killRoi()
roim.reset()
roim.close()
def threshold(imPlus, edgeThreshold=2500):
mask = Duplicator().run(imPlus)
mask_stk = mask.getStack()
# First, we threshold based on edges
IJ.setThreshold(mask, edgeThreshold, 100000, "No Update")
for i in range(mask.getImageStackSize()):
mask_stk.getProcessor(i + 1).findEdges()
IJ.run(mask, "Make Binary", "method=Default background=Default black")
# Now, we need to clean up the binary images morphologically
IJ.run(mask, "Dilate", "stack")
IJ.run(mask, "Fill Holes", "stack")
IJ.run(mask, "Erode", "stack")
IJ.run(mask, "Erode", "stack")
# Finally, remove the small particles
stk = ImageStack(mask.getWidth(), mask.getHeight())
p = PA(PA.SHOW_MASKS, 0, None, 200, 100000)
p.setHideOutputImage(True)
for i in range(mask_stk.getSize()):
mask.setSliceWithoutUpdate(i + 1)
p.analyze(mask)
mmap = p.getOutputImage()
stk.addSlice(mmap.getProcessor())
mask.setStack(stk)
mask.setSliceWithoutUpdate(1)
mask.setTitle(mask_title(imPlus.getTitle()))
mask.show()
return mask
def keep_blobs_bigger_than(imp, min_size_pix=100):
"""remove all blobs other than the largest by area"""
imp.killRoi()
rt = ResultsTable()
if "Size_filtered_" in imp.getTitle():
title_addition = ""
else:
title_addition = "Size_filtered_"
out_imp = IJ.createImage("{}{}".format(title_addition, imp.getTitle()),
imp.getWidth(), imp.getHeight(), 1, 8)
out_imp.show()
IJ.run(out_imp, "Select All", "")
IJ.run(out_imp, "Set...", "value=0 slice")
mxsz = imp.width * imp.height
roim = RoiManager()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
ParticleAnalyzer.AREA | ParticleAnalyzer.SLICE, rt,
min_size_pix, mxsz)
pa.setRoiManager(roim)
roim.reset()
rt.reset()
pa.analyze(imp)
rt_areas = rt.getColumn(rt.getColumnIndex("Area")).tolist()
# print("Number of cells identified: {}".format(len(rt_areas)));
for idx in range(len(rt_areas)):
roim.select(out_imp, idx)
IJ.run(out_imp, "Set...", "value=255 slice")
mx_ind = rt_areas.index(max(rt_areas))
roim.reset()
roim.close()
imp.changes = False
imp.close()
return out_imp
def anaParticles(imp, minSize, maxSize, minCirc, bHeadless=True):
"""anaParticles(imp, minSize, maxSize, minCirc, bHeadless=True)
Analyze particles using a watershed separation. If headless=True, we cannot
redirect the intensity measurement to the original immage becuae it is never
displayed. If we display the original, we can and get the mean gray level. We
may then compute the particle contrast from the measured Izero value for the image.
No ability here to draw outlines on the original."""
strName = imp.getShortTitle()
imp.setTitle("original")
ret = imp.duplicate()
IJ.run(ret, "Enhance Contrast", "saturated=0.35")
IJ.run(ret, "8-bit", "")
IJ.run(ret, "Threshold", "method=Default white")
IJ.run(ret, "Watershed", "")
rt = ResultsTable()
# strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
# N.B. redirect will not work without a displayed image, so we cannot use a gray level image
if bHeadless == True:
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's decimal=3"
else:
imp.show()
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
IJ.run("Set Measurements...", strSetMeas)
# note this doies not get passed directly to ParticleAnalyzer, so
# I did this, saved everything and looked for the measurement value in ~/Library/Preferences/IJ_Prefs.txt
# measurements=27355
# meas = Measurements.AREA + Measurements.CIRCULARITY + Measurements.PERIMETER + Measurements.SHAPE_DESCRIPTORS
# didn't work reliably
meas = 27355
pa = ParticleAnalyzer(0, meas, rt, minSize, maxSize, minCirc, 1.0);
pa.analyze(ret);
rt.createTableFromImage(ret.getProcessor())
return [ret, rt]
def resetpressed(event):
self.__ranges.clear()
self.__image=IJ.getImage()
rm = RoiManager.getInstance()
if (rm==None): rm = RoiManager()
rm.runCommand("reset")
self.__image.killRoi()
IJ.setAutoThreshold(self.__image, "MaxEntropy")
rt=ResultsTable()
pa=ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER+ParticleAnalyzer.CLEAR_WORKSHEET , Measurements.AREA+Measurements.ELLIPSE+Measurements.MEAN, rt, 0.00, 10000.00, 0.00, 1.00)
pa.analyze(self.__image)
self.__roisArray=[]
self.__roisArray=rm.getRoisAsArray()
#rm.runCommand("Show All")
#rm.runCommand("Select All")
#rm.runCommand("Set Color", "blue")
IJ.resetThreshold(self.__image)
keys=self.__slidersDict.keys()
for k in keys:
if k.endswith("min"):
self.__slidersDict[k].setValue(0)
self.__slidersDict[k].repaint()
else:
self.__slidersDict[k].setValue(self.__slidersDict[k].getMaximum())
self.__slidersDict[k].repaint()
def segmentNuc(impc2):
impdup = Duplicator().run(impc2)
IJ.run(impdup, "8-bit", "")
IJ.run(impdup, "Gaussian Blur...", "sigma=1.5 stack")
# AutoThresholder().getThreshold(AutoThresholder.Method.valuOf('Otsu'), int[] histogram)
IJ.setAutoThreshold(impdup, "Otsu dark")
IJ.run(impdup, "Convert to Mask", "stack")
#IJ.setAutoThreshold(impdup, "Otsu dark")
#opt = PA.SHOW_MASKS + PA.SHOW_RESULTS + PA.EXCLUDE_EDGE_PARTICLES + PA.INCLUDE_HOLES # option for stack missing
opt = PA.SHOW_MASKS + PA.EXCLUDE_EDGE_PARTICLES + PA.INCLUDE_HOLES # option for stack missing
##area mean centroid bounding integrated stack redirect=None decimal=4
meas = Meas.AREA + Meas.MEAN + Meas.CENTROID + Meas.RECT + Meas.INTEGRATED_DENSITY + Meas.STACK_POSITION
rt = ResultsTable().getResultsTable()
pa = PA(opt, meas, rt, 10.0, 300000.0, 0, 1.0)
PA.processStack = True
pa.setHideOutputImage(True)
##run("Analyze Particles...", "size=800-Infinity circularity=0.00-1.00 pixel show=Masks display exclude include stack");
outstack = ImageStack(impdup.getWidth(), impdup.getHeight())
for i in range(1,impdup.getStackSize()+1):
impdup.setSlice(i)
pa.analyze(impdup)
impbin = pa.getOutputImage()
outstack.addSlice(impbin.getProcessor())
impbin = ImagePlus("out", outstack)
IJ.run(impbin, "Invert LUT", "")
#IJ.run(impbin, "Fill Holes", "stack")
return impbin, rt
def analyzeParticles(imp, minsize, maxsize, mincirc, maxcirc,
filename='Test.czi',
addROIManager=True,
headless=True,
exclude=True):
if addROIManager is True:
if exclude is False:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_MANAGER \
+ PA.ADD_TO_OVERLAY \
if exclude is True:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_MANAGER \
+ PA.ADD_TO_OVERLAY \
+ PA.EXCLUDE_EDGE_PARTICLES
if addROIManager is False:
if exclude is False:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_OVERLAY \
if exclude is True:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_OVERLAY \
+ PA.EXCLUDE_EDGE_PARTICLES
measurements = PA.STACK_POSITION \
+ PA.LABELS \
+ PA.AREA \
+ PA.RECT \
results = ResultsTable()
p = PA(options, measurements, results, minsize, maxsize, mincirc, maxcirc)
p.setHideOutputImage(True)
particlestack = ImageStack(imp.getWidth(), imp.getHeight())
for i in range(imp.getStackSize()):
imp.setSliceWithoutUpdate(i + 1)
ip = imp.getProcessor()
#IJ.run(imp, "Convert to Mask", "")
p.analyze(imp, ip)
mmap = p.getOutputImage()
particlestack.addSlice(mmap.getProcessor())
return particlestack, results
def measure(self):
imp = IJ.openImage(self.filename)
IJ.log("Input file: %s" % self.filename)
ImageConverter(imp).convertToGray8()
res = Auto_Threshold().exec(imp, self.myMethod, self.noWhite, self.noBlack, self.doIwhite, self.doIset, self.doIlog, self.doIstackHistogram)
rt = ResultsTable()
rt.showRowNumbers(False)
pa = PA(self.options, PA.AREA + PA.PERIMETER + PA.CIRCULARITY, rt, self.MINSIZE, self.MAXSIZE)
pa.analyze(imp)
self.result = self.rtToResult(rt)
self.mask = imp
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 generate_cell_rois(seg_binary_imp):
"""generate rois from which cell shape information will be gleaned"""
seg_binary_imp.killRoi()
mxsz = seg_binary_imp.width * seg_binary_imp.height
roim = RoiManager(False)
pa_options = ParticleAnalyzer.AREA | ParticleAnalyzer.PERIMETER | ParticleAnalyzer.SHAPE_DESCRIPTORS
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, pa_options, None,
1000, mxsz)
pa.setRoiManager(roim)
roim.reset()
pa.analyze(seg_binary_imp)
rois = roim.getRoisAsArray()
roim.reset()
roim.close()
return rois
def process(inputpath, outputpath):
imp = IJ.openImage(inputpath)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
IJ.setThreshold(imp, t1, 255)
#imp.show()
#WaitForUserDialog("Title", "Look at image").show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 50, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
pa.analyze(imp)
imp.changes = False
imp.close()
areas = table.getColumn(0)
summary = {}
if areas:
summary['Image'] = filename
summary['Nuclei.count'] = len(areas)
summary['Area.Covered'] = sum(areas)
fieldnames = list(summary.keys())
with open(outputpath, 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputname + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
def countobjects(imp,
rt,
subtractBackground=False,
watershed=False,
dilate=False,
threshMethod="Otsu",
physicalUnits=True,
minSize=0.00,
maxSize=float("inf"),
minCirc=0.00,
maxCirc=1.00):
"""Threshold and count objects in channel 'channelNumber'.
This function splits an image in the separate channels, and counts the number of objects in the thresholded
channel.
Args:
imp: An ImagePlus with 1 frame, 1 slice.
Returns:
A list of filepaths.
"""
cal = imp.getCalibration()
if subtractBackground:
IJ.run(imp, "Subtract Background...", "rolling=50")
IJ.setAutoThreshold(imp, "{} dark".format(threshMethod))
IJ.run(imp, "Convert to Mask", "")
if dilate:
IJ.run(imp, "Dilate", "")
if watershed:
IJ.run(imp, "Watershed", "")
if physicalUnits: # Convert physical units to pixels for the current calibration.
minSize = cal.getRawX(math.sqrt(minSize))**2
maxSize = cal.getRawX(math.sqrt(maxSize))**2
pa = ParticleAnalyzer(
ParticleAnalyzer.SHOW_OVERLAY_OUTLINES
| ParticleAnalyzer.DISPLAY_SUMMARY, #int options
Measurements.AREA | Measurements.SHAPE_DESCRIPTORS | Measurements.MEAN
| Measurements.CENTROID | Measurements.LABELS, #int measurements
rt, #ResultsTable
minSize, #double
maxSize, #double
minCirc, #double
maxCirc) #double
pa.analyze(imp)
return imp
def get_no_nuclei_fully_enclosed(roi, full_nuclei_imp, overlap_threshold=0.65):
"""for a given cell roi and ImagePlus with binary nuclei, return how many nuclei lie ENTIRELY within the cell"""
bbox = roi.getBounds()
full_nuclei_imp.setRoi(roi)
cropped_nuc_imp = full_nuclei_imp.crop()
roi.setLocation(0, 0)
cropped_nuc_imp.setRoi(roi)
cropped_nuc_imp.killRoi()
roim = RoiManager(False)
mxsz = cropped_nuc_imp.getWidth() * cropped_nuc_imp.getHeight()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER, ParticleAnalyzer.AREA
| ParticleAnalyzer.SLICE | ParticleAnalyzer.CENTROID, None, 0, mxsz)
pa.setRoiManager(roim)
pa.analyze(cropped_nuc_imp)
cell_imp = IJ.createImage("Cell binary", cropped_nuc_imp.getWidth(),
cropped_nuc_imp.getHeight(), 1, 8)
IJ.run(cell_imp, "Select All", "")
IJ.run(cell_imp, "Set...", "value=0 slice")
cell_imp.setRoi(roi)
IJ.run(cell_imp, "Set...", "value=255 slice")
no_enclosed_nuclei = 0
for idx, nuc_roi in enumerate(roim.getRoisAsArray()):
test_imp = Duplicator().run(cell_imp)
test_imp.setRoi(nuc_roi)
IJ.run(test_imp, "Set...", "value=255 slice")
test_imp.killRoi()
IJ.run(test_imp, "Create Selection", "")
IJ.run(test_imp, "Make Inverse", "")
test_roi = test_imp.getRoi()
test_roi_stats = test_roi.getStatistics()
cell_roi_stats = roi.getStatistics()
nuc_roi_stats = nuc_roi.getStatistics()
if test_roi_stats.area < (
cell_roi_stats.area +
(1 - overlap_threshold) * nuc_roi_stats.area
): # i.e. if more than (100*overlap_threshold)% of nucleus is inside cell...
no_enclosed_nuclei += 1
test_imp.changes = False
test_imp.close()
roi.setLocation(bbox.getX(), bbox.getY())
cropped_nuc_imp.changes = False
cropped_nuc_imp.close()
cell_imp.changes = False
cell_imp.close()
return no_enclosed_nuclei
def removeSmallCCs(image):
MINSIZE = 1000
MAXSIZE = 1000000
options = PA.SHOW_ROI_MASKS
results = ResultsTable()
p = PA(options, PA.STACK_POSITION + PA.LABELS + PA.AREA + PA.PERIMETER + PA.CIRCULARITY, results, MINSIZE, MAXSIZE)
p.setHideOutputImage(True)
p.analyze(image)
mmap = p.getOutputImage()
mip = mmap.getProcessor()
mip.threshold(0)
img = ImagePlus("rods_processed", mip)
IJ.run(img, "8-bit", "")
IJ.run(img, "Make Binary", "method=Default background=Dark black")
return img
def AnalyzeParticle(IMP):
rm = RoiManager().getInstance2()
rt = ResultsTable()
#再現性確保のために最終的には実装
#IJ.run("Set Measurements...","area centroid fit redirect=None decimal=3")
#https://imagej.nih.gov/ij/developer/api/constant-values.html#ij.plugin.filter.ParticleAnalyzer.SHOW_RESULTS
#表示オプション無し、resultは全部選択
PA = ParticleAnalyzer(0 , 1043199 , rt, 10000, 300000, 0.2, 1.0)
PA.setRoiManager(rm)
PA.analyze(IMP)
#IJ.run(IMP, "Analyze Particles...", "display clear include add")
rm.runCommand("Save", "C:/Users/For Programming/Documents/Python Scripts/OutletHDD/aaa.zip")
rt.saveAs("C:/Users/For Programming/Documents/Python Scripts/OutletHDD/aaa.csv")
#最後に全ての結果をCloseする。
#写真を先に消さないとバグる。
IMP.close()
rm.close()
rt.reset()
def anaParticles(imp, minSize, maxSize, minCirc):
strName = imp.getShortTitle()
imp.setTitle("original")
ret = imp.duplicate()
IJ.run(ret, "Enhance Contrast", "saturated=0.35")
IJ.run(ret, "8-bit", "")
IJ.run(ret, "Threshold", "method=Default white")
IJ.run(ret, "Watershed", "")
rt = ResultsTable()
# strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
# N.B. redirect will not work without a displayed image, so we cannot use a gray level image
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's decimal=3"
IJ.run("Set Measurements...", strSetMeas)
# note this doies not get passed directly to ParticleAnalyzer, so
# I did this, saved everything and looked for the measurement value in ~/Library/Preferences/IJ_Prefs.txt
# measurements=27355
# meas = Measurements.AREA + Measurements.CIRCULARITY + Measurements.PERIMETER + Measurements.SHAPE_DESCRIPTORS
# didn't work reliably
meas = 27355
pa = ParticleAnalyzer(0, meas, rt, minSize, maxSize, minCirc, 1.0);
pa.analyze(ret);
rt.createTableFromImage(ret.getProcessor())
return [ret, rt]
def ana_particles(imp, minSize, maxSize, minCirc, bHeadless=True):
"""ana_particles(imp, minSize, maxSize, minCirc, bHeadless=True)
Analyze particles using a watershed separation. If headless=True, we cannot
redirect the intensity measurement to the original image because it is never
displayed. If we display the original, we can and get the mean gray level. We
may then compute the particle contrast from the measured Izero value for the image.
No ability here to draw outlines on the original.
"""
strName = imp.getShortTitle()
imp.setTitle("original")
ret = imp.duplicate()
ret.setTitle("work")
IJ.run(ret, "Enhance Contrast", "saturated=0.35")
IJ.run(ret, "8-bit", "")
IJ.run(ret, "Threshold", "method=Default white")
IJ.run(ret, "Watershed", "")
rt = ResultsTable()
# strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
# N.B. redirect will not work without a displayed image, so we cannot use a gray level image
if bHeadless == True:
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's decimal=3"
else:
imp.show()
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
IJ.run("Set Measurements...", strSetMeas)
# note this does not get passed directly to ParticleAnalyzer, so
# I did this, saved everything and looked for the measurement value in ~/Library/Preferences/IJ_Prefs.txt
# measurements=27355
# meas = Measurements.AREA + Measurements.CIRCULARITY + Measurements.PERIMETER + Measurements.SHAPE_DESCRIPTORS
# didn't work reliably
meas = 27355
pa = ParticleAnalyzer(0, meas, rt, minSize, maxSize, minCirc, 1.0)
pa.analyze(ret)
rt.createTableFromImage(ret.getProcessor())
return [ret, rt]
def countParticles(imp, roim, minSize, maxSize, minCircularity,
maxCircularity):
# Create a table to store the results
table = ResultsTable()
# Create the particle analyzer
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA | Measurements.MEAN | Measurements.ELLIPSE, table,
minSize, maxSize, minCircularity, maxCircularity)
pa.setRoiManager(roim)
pa.setHideOutputImage(True)
if pa.analyze(imp):
print "All ok"
else:
print "There was a problem in analyzing", blobs
areas = table.getColumn(0)
intensities = table.getColumn(1)
majors = table.getColumn(2)
def countParticles(imp, roim, minSize, maxSize, minCircularity, maxCircularity): # Create a table to store the results table = ResultsTable() # Create the particle analyzer pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA|Measurements.MEAN, table, minSize, maxSize, minCircularity, maxCircularity) #pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA|Measurements.MEAN, table, 10, Double.POSITIVE_INFINITY, 0.5, 1.0) #pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA|Measurements.MEAN, table, 5, 6, 0.5, 1.0) pa.setRoiManager(roim) pa.setHideOutputImage(True) if pa.analyze(imp): print "All ok" else: print "There was a problem in analyzing", blobs areas = table.getColumn(0) intensities = table.getColumn(1) if ( (areas!=None) and (intensities!=None)): for area, intensity in zip(areas,intensities): print str(area)+": "+str(intensity)
def getParticleCenters(imp):
# Create a table to store the results
rt = ResultsTable()
paOpts = PA.SHOW_OUTLINES \
+ PA.INCLUDE_HOLES \
+ PA.EXCLUDE_EDGE_PARTICLES
measurements = PA.CENTROID + PA.CENTER_OF_MASS
MINSIZE = 1000
MAXSIZE = Double.POSITIVE_INFINITY
pa = PA(paOpts,measurements, rt, MINSIZE, MAXSIZE)
pa.setHideOutputImage(True)
if not pa.analyze(imp):
print "There was a problem in analyzing", imp
# The measured centroids are listed in the first column of the results table, as a float array:
centroids_x = rt.getColumn(rt.X_CENTROID)
centroids_y = rt.getColumn(rt.Y_CENTROID)
coms_x = rt.getColumn(rt.X_CENTER_OF_MASS)
coms_y = rt.getColumn(rt.Y_CENTER_OF_MASS)
return (centroids_x,centroids_y, coms_x, coms_y)
def process(self,imp):
# extract nucleus channel, 8-bit and twice binned
imp.setC(self.nucleusChannel)
ip = imp.getChannelProcessor().duplicate()
ip = ip.convertToByteProcessor()
ip = ip.bin(4)
nucleus = ImagePlus("nucleus_channel", ip)
# threshold image and separate clumped nuclei
IJ.run(nucleus, "Auto Threshold", "method=Otsu white setthreshold show");
IJ.run(nucleus, "Make Binary", "thresholded remaining black");
IJ.run(nucleus, "Watershed", "");
directory = imp.getTitle()
directory = directory.replace(" ", "_")\
.replace(",", "_")\
.replace("#", "_series")\
.replace("...", "")\
.replace(".","_")
directory = os.path.join(self.exportDir, directory)
sliceDirectory = os.path.join(directory, "slices")
print directory
print sliceDirectory
if not os.path.exists(sliceDirectory):
os.makedirs(sliceDirectory)
# Create a table to store the results
table = ResultsTable()
# Create a hidden ROI manager, to store a ROI for each blob or cell
#roim = RoiManager(True)
# remove small particles and border particles
pa = ParticleAnalyzer(\
ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES,\
Measurements.CENTER_OF_MASS,\
table,\
self.minArea, self.maxArea,\
0.0,1.0)
if pa.analyze(nucleus):
print "All ok, number of particles: ", table.size()
else:
print "There was a problem in analyzing", imp, nucleus
table.save(os.path.join(directory, "rt.csv"))
# read the center of mass coordinates
cmx = table.getColumn(0)
cmy = table.getColumn(1)
if self.debug:
imp.show()
i=0
for i in range(0, min(self.nCells,table.size())):
# ROI around the cell
cmx = table.getValue("XM",i)
cmy = table.getValue("YM",i)
x = 4 * cmx - (self.boxSize - 1) / 2
y = 4 * cmy - (self.boxSize - 1) / 2
if (x < self.edge or y < self.edge or x > imp.getWidth() - self.edge or y > imp.getHeight() - self.edge):
continue
roi = Roi(x,y,self.boxSize,self.boxSize)
imp.setRoi(roi, False)
cellStack = ImageStack(self.boxSize, self.boxSize)
for z in range(1, imp.getNSlices() + 1):
imp.setSlice(z)
for c in range(1, imp.getNChannels() + 1):
imp.setC(c)
# copy ROI to stack
imp.copy()
impSlice = imp.getClipboard()
cellStack.addSlice(impSlice.getProcessor())
if self.slices:
sliceTitle = "cell_%s_z%s_c%s" % (str(i).zfill(4), str(z).zfill(3), str(c))
print sliceTitle
IJ.saveAsTiff(impSlice, os.path.join(sliceDirectory, sliceTitle))
impSlice.close()
title = "cell_" + str(i).zfill(4)
cell = ImagePlus(title, cellStack)
# save ROI image
IJ.saveAsTiff(cell, os.path.join(directory, title))
cell.close()
if self.debug:
imp.updateAndDraw()
wait = Wait("particle done")
wait.show()
def process(subFolder, outputDirectory, filename):
#IJ.close()
imp = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", ".tif"))
imp.show()
# Get the pixel values from the xml file
for file in os.listdir(inputDirectory + subFolder):
if file.endswith('.xml'):
xml = os.path.join(inputDirectory + subFolder, file)
xml = "C:/Users/Harris/Desktop/test_xml_for_parsing_pixel.xml"
element_tree = ET.parse(xml)
root = element_tree.getroot()
for dimensions in root.iter('DimensionDescription'):
num_pixels = int(dimensions.attrib['NumberOfElements'])
if dimensions.attrib['Unit'] == "m":
length = float(dimensions.attrib['Length']) * 1000000
else:
length = float(dimensions.attrib['Length'])
pixel_length = length / num_pixels
else:
pixel_length = 0.8777017
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=" +
str(pixel_length) + " pixel_height=" + str(pixel_length) +
" voxel_depth=25400.0508001")
ic = ImageConverter(imp)
ic.convertToGray8()
#IJ.setThreshold(imp, 2, 255)
#Automatically selects the area of the organoid based on automated thresholding and creates a mask to be applied on
#all other images
IJ.setAutoThreshold(imp, "Mean dark no-reset")
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Analyze Particles...", "size=100000-Infinity add select")
rm = RoiManager.getInstance()
num_roi = rm.getCount()
for i in num_roi:
imp = getCurrentImage()
rm.select(imp, i)
IJ.setBackgroundColor(0, 0, 0)
IJ.run(imp, "Clear Outside", "")
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Dark")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Bright")
# Save the mask and open it
IJ.saveAs("tiff", inputDirectory + '/mask' + i)
mask = IJ.openImage(inputDirectory + '/mask' + i + '.tif')
if not displayImages:
imp.changes = False
imp.close()
images = [None] * 5
intensities = [None] * 5
blobsarea = [None] * 5
blobsnuclei = [None] * 5
bigAreas = [None] * 5
imp.close()
# Loop to open all the channel images
for chan in channels:
v, x = chan
images[x] = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", "_ch0" +
str(x) + ".tif"))
# Apply Mask on all the images and save them into an array
apply_mask = ImageCalculator()
images[x] = apply_mask.run("Multiply create 32 bit", mask,
images[x])
ic = ImageConverter(images[x])
ic.convertToGray8()
imp = images[x]
# Calculate the intensities for each channel as well as the organoid area
for roi in rm.getRoisAsArray():
imp.setRoi(roi)
stats_i = imp.getStatistics(Measurements.MEAN
| Measurements.AREA)
intensities[x] = stats_i.mean
bigAreas[x] = stats_i.area
rm.close()
# Opens the ch00 image and sets default properties
#Get the pixel values from the xml file
for file in os.listdir(subFolder):
if file.endswith('.xml'):
xml = os.path.join(inputDirectory + subFolder, file)
xml = "C:/Users/Harris/Desktop/test_xml_for_parsing_pixel.xml"
element_tree = ET.parse(xml)
root = element_tree.getroot()
for dimensions in root.iter('DimensionDescription'):
num_pixels = int(dimensions.attrib['NumberOfElements'])
if dimensions.attrib['Unit'] == "m":
length = float(dimensions.attrib['Length']) * 1000000
else:
length = float(dimensions.attrib['Length'])
pixel_length = length / num_pixels
else:
pixel_length = 0.8777017
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
imp = apply_mask.run("Multiply create 32 bit", mask, imp)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=" +
str(pixel_length) + "pixel_height=" + str(pixel_length) +
"voxel_depth=25400.0508001")
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.run(imp, "Remove Outliers...",
"radius=2" + " threshold=50" + " which=Dark")
IJ.run(imp, "Gaussian Blur...", "sigma=" + str(blur))
IJ.setThreshold(imp, lowerBounds[0], 255)
if displayImages:
imp.show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
if not displayImages:
imp.changes = False
imp.close()
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15, 9999999999999999,
0.2, 1.0)
pa.setHideOutputImage(True)
# imp = impM
# imp.getProcessor().invert()
pa.analyze(imp)
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the ch00 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = [None] * 5
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = images[x]
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, lowerBounds[x], 255)
if displayImages:
imp.show()
WaitForUserDialog("Title",
"Adjust Threshold for Marker " + v).show()
IJ.run(imp, "Convert to Mask", "")
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray[x] = areaFractions
roim.close()
for chan in channels:
v, x = chan
imp = images[x]
imp.deleteRoi()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15,
9999999999999999, 0.2, 1.0)
pa.analyze(imp)
blobs = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA)
blobs.append(stats.area)
blobsarea[x] = sum(
blobs
) #take this out and use intial mask tissue area from the beginning
blobsnuclei[x] = len(blobs)
if not displayImages:
imp.changes = False
imp.close()
roim.reset()
roim.close()
imp.close()
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
summary['Image'] = filename
summary['Directory'] = subFolder
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Name', 'Directory', 'Image', 'size-average',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
summary["organoid-area"] = bigAreas[x]
fieldnames.append("organoid-area")
for chan in channels:
v, x = chan
summary[v + "-positive"] = 0
fieldnames.append(v + "-positive")
summary[v + "-intensity"] = intensities[x]
fieldnames.append(v + "-intensity")
summary[v + "-blobsarea"] = blobsarea[x]
fieldnames.append(v + "-blobsarea")
summary[v + "-blobsnuclei"] = blobsnuclei[x]
fieldnames.append(v + "-blobsnuclei")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
for z, area in enumerate(areas):
log.write(str(area))
log.write("\n")
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for chan in channels:
v, x = chan
if areaFractionsArray[x][z] > areaFractionThreshold[
0]: # theres an error here im not sure why. i remember fixing it before
summary[chan[0] + '-positive'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
summary[channels[1][0] + '-' + channels[2][0] +
'-' + channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputDirectory + "/" + outputName + ".csv", 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputName + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
IJ.run(imp, "Close All", "")
def analyzeParticles(imp,
minsize,
maxsize,
mincirc,
maxcirc,
#filename='Test.czi',
addROIManager=False,
#headless=False,
exclude=True):
if GraphicsEnvironment.isHeadless():
print('Headless Mode detected. Do not use ROI Manager.')
addROIManager = False
if addROIManager:
# get the ROI manager instance
rm = RoiManager.getInstance()
if rm is None:
rm = RoiManager()
rm.runCommand("Associate", "true")
if not exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_MANAGER \
+ PA.ADD_TO_OVERLAY \
if exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_MANAGER \
+ PA.ADD_TO_OVERLAY \
+ PA.EXCLUDE_EDGE_PARTICLES
if not addROIManager:
if not exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_OVERLAY \
if exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_OVERLAY \
+ PA.EXCLUDE_EDGE_PARTICLES
measurements = PA.STACK_POSITION \
+ PA.LABELS \
+ PA.AREA \
+ PA.RECT \
+ PA.PERIMETER \
+ PA.SLICE \
+ PA.SHAPE_DESCRIPTORS \
+ PA.CENTER_OF_MASS \
+ PA.CENTROID
results = ResultsTable()
p = PA(options, measurements, results, minsize, maxsize, mincirc, maxcirc)
p.setHideOutputImage(True)
particlestack = ImageStack(imp.getWidth(), imp.getHeight())
for i in range(imp.getStackSize()):
imp.setSliceWithoutUpdate(i + 1)
ip = imp.getProcessor()
# convert to a mask for the particle analyzer
ip.invert()
# do the particle analysis
p.analyze(imp, ip)
mmap = p.getOutputImage()
# add the slide to the full stack
particlestack.addSlice(mmap.getProcessor())
return particlestack, results
from ij.measure import ResultsTable from ij.measure import Measurements from ij.plugin.frame import RoiManager from ij.plugin.filter import ParticleAnalyzer from ij import IJ # get active image imp=IJ.getImage() # set up first ROI manager table1 = ResultsTable() roim1=RoiManager() pa1 = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA|Measurements.MEAN|Measurements.ELLIPSE, table1, 0, 100, 0, 1) pa1.setRoiManager(roim1) pa1.analyze(imp) # set up second ROI manager table2 = ResultsTable() # Pass true to second ROI manager so it will not be seen roim2=RoiManager(True) pa2 = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA|Measurements.MEAN|Measurements.ELLIPSE, table2, 100, 500, 0, 1) pa2.setRoiManager(roim2) pa2.analyze(imp) print "rois from first manager:" for roi in roim1.getRoisAsArray(): print roi print print "rois from second manager:" for roi in roim2.getRoisAsArray(): print roi
def process(subFolder, outputDirectory, filename):
imp = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", ".tif"))
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, 2, 255)
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Dark")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Bright")
imp.getProcessor().invert()
rm = RoiManager(True)
imp.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
boundroi = ThresholdToSelection.run(imp)
rm.addRoi(boundroi)
if not displayImages:
imp.changes = False
imp.close()
images = [None] * 5
intensities = [None] * 5
blobsarea = [None] * 5
blobsnuclei = [None] * 5
bigAreas = [None] * 5
for chan in channels:
v, x = chan
images[x] = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", "_ch0" +
str(x) + ".tif"))
imp = images[x]
for roi in rm.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.MEAN | Measurements.AREA)
intensities[x] = stats.mean
bigAreas[x] = stats.area
rm.close()
# Opens the ch00 image and sets default properties
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.run(imp, "Remove Outliers...",
"radius=2" + " threshold=50" + " which=Dark")
IJ.run(imp, "Gaussian Blur...", "sigma=" + str(blur))
IJ.setThreshold(imp, lowerBounds[0], 255)
if displayImages:
imp.show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
if not displayImages:
imp.changes = False
imp.close()
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 15, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
#imp = impM
# imp.getProcessor().invert()
pa.analyze(imp)
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the ch00 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = [None] * 5
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = images[x]
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, lowerBounds[x], 255)
if displayImages:
imp.show()
WaitForUserDialog("Title",
"Adjust Threshold for Marker " + v).show()
IJ.run(imp, "Convert to Mask", "")
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray[x] = areaFractions
roim.close()
for chan in channels:
v, x = chan
imp = images[x]
imp.deleteRoi()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15, 9999999999999999,
0.2, 1.0)
pa.analyze(imp)
blobs = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA)
blobs.append(stats.area)
blobsarea[x] = sum(blobs)
blobsnuclei[x] = len(blobs)
if not displayImages:
imp.changes = False
imp.close()
roim.reset()
roim.close()
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
summary['Image'] = filename
summary['Directory'] = subFolder
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Name', 'Directory', 'Image', 'size-average',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
summary["organoid-area"] = bigAreas[x]
fieldnames.append("organoid-area")
for chan in channels:
v, x = chan
summary[v + "-positive"] = 0
fieldnames.append(v + "-positive")
summary[v + "-intensity"] = intensities[x]
fieldnames.append(v + "-intensity")
summary[v + "-blobsarea"] = blobsarea[x]
fieldnames.append(v + "-blobsarea")
summary[v + "-blobsnuclei"] = blobsnuclei[x]
fieldnames.append(v + "-blobsnuclei")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
for z, area in enumerate(areas):
log.write(str(area))
log.write("\n")
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for chan in channels:
v, x = chan
if areaFractionsArray[x][z] > areaFractionThreshold[
0]: #theres an error here im not sure why. i remember fixing it before
summary[chan[0] + '-positive'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
summary[channels[1][0] + '-' + channels[2][0] +
'-' + channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputDirectory + "/" + outputName + ".csv", 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputName + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
+ PA.CLEAR_WORKSHEET \
# + PA.SHOW_RESULTS \
rt = ResultsTable()
p = PA(options, PA.AREA + PA.STACK_POSITION, rt, MINSIZE, MAXSIZE)
p.setHideOutputImage(True)
# Morphological dilate
binner.setup('dilate', None)
clusters = 0
initialCells = 0
# dilate by 'SAMPLEITER'
for i in range(SAMPLEITER+1):
p.analyze(binimp)
cellcounts = rt.getCounter()
if i == 0:
initialCells = cellcounts
#IJ.log("iter:" + str(i) + " -- cell counts: " + str(cellcounts))
if i == SAMPLEITER:
clusters = cellcounts
binner.run(binimp.getProcessor())
rt.reset()
#binimp.show()
#binorg.show()
IJ.log("==== " + imp3.getTitle() + " =====")
IJ.log("Number of Nucleus : " + str(initialCells))
IJ.log("Clusters at dilation " + str(SAMPLEITER) + ": " + str(clusters))
IJ.log("Clusters/Nucleus " + str(float(clusters)/float(initialCells)))
def process(subDir, subsubDir, outputDirectory, filename):
subFolder = subDir + "/" + subsubDir
# Opens the d0 image and sets default properties
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
ic = ImageConverter(imp)
ic.convertToGray8()
imp.updateAndDraw()
dup = imp.duplicate()
IJ.run(
dup, "Convolve...",
"text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize"
)
stats = dup.getStatistics(Measurements.MEAN | Measurements.MIN_MAX
| Measurements.STD_DEV)
dup.close()
blurry = (stats.mean < 18 and stats.stdDev < 22) or stats.max < 250
IJ.setThreshold(imp, lowerBounds[0], 255)
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
if displayImages:
imp.show()
WaitForUserDialog("Title", "Look at image").show()
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 15, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
pa.analyze(imp)
if not displayImages:
imp.changes = False
imp.close()
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the d0 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = []
areaMeansArray = []
means = []
totalAreas = []
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = IJ.openImage(inputDirectory + subFolder + '/' +
filename.replace("d0.TIF", "d" + str(x) + ".TIF"))
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
imp.updateAndDraw()
stats = imp.getStatistics(Measurements.MEAN)
means.append(stats.mean)
areaMeans = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.MEAN)
areaMeans.append(stats.mean)
IJ.setThreshold(imp, lowerBounds[x], 255)
IJ.run(imp, "Convert to Mask", "")
if displayImages:
imp.show()
WaitForUserDialog("Title", "Look at image").show()
stats = imp.getStatistics(Measurements.AREA_FRACTION)
totalAreas.append(stats.areaFraction)
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray.append(areaFractions)
areaMeansArray.append(sum(areaMeans) / len(areaMeans))
if not displayImages:
imp.changes = False
imp.close()
roim.close()
# Figures out what well the image is a part of
ind = filename.index("p00_0_")
row = filename[ind + 6:ind + 7]
column = str(int(filename[ind + 7:ind + 9]))
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
# Finds the name of the well from the nameArray 2d array
if row in nameArray:
if column in nameArray[row]:
summary['Name'] = nameArray[row][column]
summary['Image'] = filename
summary['Directory'] = subDir
summary['SubDirectory'] = subsubDir
summary['Row'] = row
summary['Column'] = column
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
summary['image-quality'] = blurry
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Name', 'Directory', 'SubDirectory', 'Image', 'Row', 'Column',
'size-average', 'image-quality',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
for chan in channels:
v, x = chan
summary[v + "-positive"] = 0
summary[v + "-intensity"] = means[x]
summary[v + "-area"] = totalAreas[x]
summary[v + "-intensity-in-nuclei"] = areaMeansArray[x]
summary[v + "-area-fraction-in-nuclei"] = sum(
areaFractionsArray[x]) / len(areaFractionsArray[x])
fieldnames.append(v + "-positive")
fieldnames.append(v + "-intensity")
fieldnames.append(v + "-area")
fieldnames.append(v + "-intensity-in-nuclei")
fieldnames.append(v + "-area-fraction-in-nuclei")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
if not (areas is None):
for z, area in enumerate(areas):
if not (area is None or summary is None):
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for y, chan in enumerate(channels):
v, x = chan
if areaFractionsArray[y][z] > areaFractionThreshold:
summary[chan[0] + '-positive'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold:
if areaFractionsArray[2][z] > areaFractionThreshold:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold:
if areaFractionsArray[3][z] > areaFractionThreshold:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold:
if areaFractionsArray[3][z] > areaFractionThreshold:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][
z] > areaFractionThreshold:
summary[channels[1][0] + '-' +
channels[2][0] + '-' +
channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputDirectory + "/" + outputName + ".csv", 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputName + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
def merge_incorrect_splits_and_get_centroids(imp,
centroid_distance_limit=100,
size_limit=100):
"""if particles are found with centroids closer than centroid_distance_limit and both have size<size_limit, get average centroid"""
imp.killRoi()
rt = ResultsTable()
out_imp = IJ.createImage("Nuclei centroids from {}".format(imp.getTitle()),
imp.getWidth(), imp.getHeight(), 1, 8)
out_imp.show()
IJ.run(out_imp, "Select All", "")
IJ.run(out_imp, "Set...", "value=0 slice")
out_imp.show()
cal = imp.getCalibration()
mxsz = imp.width * cal.pixelWidth * imp.height * cal.pixelHeight
print("mxsz = {}".format(mxsz))
roim = RoiManager()
imp.show()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER, ParticleAnalyzer.AREA
| ParticleAnalyzer.SLICE | ParticleAnalyzer.CENTROID, rt, 0,
size_limit)
pa.setRoiManager(roim)
roim.reset()
rt.reset()
pa.analyze(imp)
MyWaitForUser("paise",
"pause post-merge incorrect splits particel analysis")
rt_xs = rt.getColumn(rt.getColumnIndex("X")).tolist()
rt_ys = rt.getColumn(rt.getColumnIndex("Y")).tolist()
centroids = [(x, y) for x, y in zip(rt_xs, rt_ys)]
print("centroids = {}".format(centroids))
centroids_set = set()
for c in centroids:
ds = [
math.sqrt((c[0] - cx)**2 + (c[1] - cy)**2)
for (cx, cy) in centroids
]
close_mask = [d < centroid_distance_limit for d in ds]
# if no other centroids are within centroid_distance_limit, add this centroid to the output set
# otherwise, add the average position of this centroid and those within centroid_distance_limit to the output set
centroids_set.add(
(sum([msk * b[0]
for msk, b in zip(close_mask, centroids)]) / sum(close_mask),
sum([msk * b[1] for msk, b in zip(close_mask, centroids)]) /
sum(close_mask)))
roim.reset()
rt.reset()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER, ParticleAnalyzer.AREA
| ParticleAnalyzer.SLICE | ParticleAnalyzer.CENTROID, rt, size_limit,
mxsz)
pa.setRoiManager(roim)
pa.analyze(imp)
MyWaitForUser("paise",
"pause post-merge incorrect splits particel analysis 2")
if rt.columnExists("X"):
rt_xs = rt.getColumn(rt.getColumnIndex("X")).tolist()
rt_ys = rt.getColumn(rt.getColumnIndex("Y")).tolist()
centroids = [(x, y) for x, y in zip(rt_xs, rt_ys)]
for c in centroids:
centroids_set.add(c)
centroids = list(centroids_set)
cal = imp.getCalibration()
centroids = [(c[0] / cal.pixelWidth, c[1] / cal.pixelHeight)
for c in centroids]
print("new number of nuclei identified = {}".format(len(centroids)))
roim.reset()
roim.close()
for idx, c in enumerate(centroids):
roi = OvalRoi(c[0], c[1], 10, 10)
out_imp.setRoi(roi)
IJ.run(out_imp, "Set...", "value={} slice".format(idx + 1))
imp.changes = False
#imp.close();
return out_imp
BS.rollingBallBackground(dapi_ip,
*[a[1] for a in bkgd_sub])
if SHOW_IMAGES:
dapi.updateAndDraw()
# IJ.setAutoThreshold("Otsu");
# IJ.run("Convert to Mask");
dapi_ip.autoThreshold()
dapi.setProcessor(dapi_ip.convertToByteProcessor(False))
dapi_ip = dapi.getProcessor()
# IJ.run("Watershed");
EDM().toWatershed(dapi_ip)
dapi_ip.invert()
# IJ.run("Analyze Particles...", "size=30-400 show=Masks");
pa_options = particle_analyzer(dapi.getCalibration().pixelWidth)
PA = ParticleAnalyzer(*[a[1] for a in pa_options])
PA.analyze(dapi, dapi_ip)
if SHOW_IMAGES:
dapi.updateAndDraw()
IJ.save(dapi, savename(stack))
dapi.close()
rename_file(progress_name, done_name)
print 'done'
# 1. options (could be SHOW_ROI_MASKS, SHOW_OUTLINES, SHOW_MASKS, SHOW_NONE, ADD_TO_MANAGER, and others; combined with bitwise-or)
# 2. measurement options (see [http://imagej.net/developer/api/ij/measure/Measurements.html Measurements])
# 3. a ResultsTable to store the measurements
# 4. The minimum size of a particle to consider for measurement
# 5. The maximum size (idem)
# 6. The minimum circularity of a particle
# 7. The maximum circularity
minSize = 30.0
maxSize = 10000.0
opts = ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES | ParticleAnalyzer.SHOW_OVERLAY_OUTLINES
print(opts)
meas = Measurements.AREA | Measurements.MEAN | Measurements.CENTER_OF_MASS
print(meas)
pa = ParticleAnalyzer(opts, meas, results_table, minSize, maxSize)
# pa.setHideOutputImage(False)
pa.setRoiManager(roim)
if pa.analyze(imp_work):
imp_out = pa.getOutputImage()
# imp_out.show()
roim.runCommand(blobs, "Show All with labels")
blobs.show()
results_table.show("Results")
roim.show()
print "All ok"
else:
print "There was a problem in analyzing", blobs
# The measured areas are listed in the first column of the results table, as a float array:
areas = results_table.getColumn(0)
import ij
from ij import IJ
import ij.plugin
import ij.gui
import ij.measure
from ij.plugin.filter import ParticleAnalyzer as PA
# Make the background black...I'm not sure why it doesn't do this automatically!
IJ.run("Options...", "iterations=1 black count=1")
options = PA.FOUR_CONNECTED + \
PA.INCLUDE_HOLES + \
PA.SHOW_OUTLINES
rt = ij.measure.ResultsTable()
analyzer = PA(options, PA.AREA, rt, 0, 10**9)
image = IJ.getImage()
analyzer.analyze(image)
#IJ.run("Analyze Particles...", "include add slice");
def process(subFolder, outputDirectory, filename):
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
imp.show()
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
dup = imp.duplicate()
dup_title = dup.getTitle()
ic.convertToGray8()
imp.updateAndDraw()
IJ.run("Threshold...")
IJ.setThreshold(218, 245)
IJ.run(imp, "Convert to Mask", "")
rm = RoiManager()
imp.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
boundroi = ThresholdToSelection.run(imp)
rm.addRoi(boundroi)
imp.changes = False
imp.close()
images = [None] * 5
intensities = [None] * 5
blobsarea = [None] * 5
blobsnuclei = [None] * 5
cells = [None] * 5
bigareas = [None] * 5
IJ.run(dup, "Colour Deconvolution", "vectors=[H DAB]")
images[0] = getImage(dup_title + "-(Colour_1)")
images[1] = getImage(dup_title + "-(Colour_2)")
images[2] = getImage(dup_title + "-(Colour_3)")
images[2].close()
for chan in channels:
v, x = chan
imp = images[x]
imp.show()
for roi in rm.getRoiManager().getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.MEAN | Measurements.AREA)
intensities[x] = stats.mean
bigareas[x] = stats.area
rm.runCommand(imp, "Show None")
rm.close()
# Opens the ch00 image and sets default properties
imp = images[0].duplicate()
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
imp.show()
setTempCurrentImage(imp)
ic = ImageConverter(imp)
imp.updateAndDraw()
IJ.run(imp, "Gaussian Blur...", "sigma=" + str(blur))
imp.updateAndDraw()
imp.show()
IJ.run("Threshold...")
IJ.setThreshold(30, lowerBounds[0])
if displayImages:
imp.show()
WaitForUserDialog(
"Title", "Adjust threshold for nuclei. Current region is: " +
region).show()
IJ.run(imp, "Convert to Mask", "")
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 5, 9999999999999999, 0.05, 1.0)
pa.setHideOutputImage(True)
imp = IJ.getImage()
# imp.getProcessor().invert()
pa.analyze(imp)
imp.changes = False
imp.close()
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the ch00 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = [None] * 5
maxThresholds = []
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = images[x]
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
imp.show()
setTempCurrentImage(imp)
ic = ImageConverter(imp)
ic.convertToGray8()
imp.updateAndDraw()
rm.runCommand(imp, "Show None")
rm.runCommand(imp, "Show All")
rm.runCommand(imp, "Show None")
imp.show()
IJ.selectWindow(imp.getTitle())
IJ.run("Threshold...")
IJ.setThreshold(20, lowerBounds[x])
if displayImages:
WaitForUserDialog(
"Title", "Adjust threshold for " + v +
". Current region is: " + region).show()
ip = imp.getProcessor()
maxThresholds.append(ip.getMaxThreshold())
IJ.run(imp, "Convert to Mask", "")
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoiManager().getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray[x] = areaFractions
roim.close()
for chan in channels:
v, x = chan
imp = images[x]
imp.deleteRoi()
imp.updateAndDraw()
setTempCurrentImage(imp)
roim = RoiManager()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15, 9999999999999999,
0.2, 1.0)
pa.analyze(imp)
blobs = []
cell = []
for roi in roim.getRoiManager().getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA)
blobs.append(stats.area)
if stats.area > tooSmallThresholdDAB and stats.area < tooBigThresholdDAB:
cell.append(stats.area)
blobsarea[x] = sum(blobs)
blobsnuclei[x] = len(blobs)
cells[x] = len(cell)
imp.changes = False
imp.close()
roim.reset()
roim.close()
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
summary['Image'] = filename
summary['Directory'] = subFolder
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Directory', 'Image', 'size-average',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
for row in info:
if row['Animal ID'] == filename.replace('s', '-').replace(
'p', '-').split('-')[0]:
for key, value in row.items():
fieldnames.insert(0, key)
summary[key] = value
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
summary["tissue-area"] = bigareas[0]
fieldnames.append("tissue-area")
for chan in channels:
v, x = chan
summary[v + "-HEMO-cells"] = 0
fieldnames.append(v + "-HEMO-cells")
summary[v + "-intensity"] = intensities[x]
fieldnames.append(v + "-intensity")
summary[v + "-area"] = blobsarea[x]
fieldnames.append(v + "-area")
summary[v + "-area/tissue-area"] = blobsarea[x] / bigareas[0]
fieldnames.append(v + "-area/tissue-area")
summary[v + "-particles"] = blobsnuclei[x]
fieldnames.append(v + "-particles")
summary[v + "-cells"] = cells[x]
fieldnames.append(v + "-cells")
summary[v + "-particles/tissue-area"] = blobsnuclei[x] / bigareas[0]
fieldnames.append(v + "-particles/tissue-area")
fieldnames.append(v + "-HEMO-Cells/tissue-area")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
for z, area in enumerate(areas):
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for chan in channels:
v, x = chan
if areaFractionsArray[x][z] > areaFractionThreshold[0]:
summary[chan[0] + '-HEMO-cells'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
summary[channels[1][0] + '-' + channels[2][0] +
'-' + channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
for chan in channels:
v, x = chan
summary[v + "-cells/tissue-area"] = summary[v + "-cells"] / bigareas[0]
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
if displayImages:
fieldnames = ["Directory", "Image"]
for chan in channels:
v, x = chan
summary[v + "-threshold"] = maxThresholds[x]
fieldnames.append(v + "-threshold")
allMaxThresholds[v + "-" + region].append(maxThresholds[x])
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputName, 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputName) < 1:
writer.writeheader()
writer.writerow(summary)
def processImages(cfg, wellName, wellPath, images):
stats = [[[dict() for t in range(cfg.getValue(ELMConfig.numT))] for z in range(cfg.getValue(ELMConfig.numZ))] for c in range(cfg.getValue(ELMConfig.numChannels))]
times = {}
for c in range(0, cfg.getValue(ELMConfig.numChannels)):
chanStr = 'ch%(channel)02d' % {"channel" : c};
chanName = cfg.getValue(ELMConfig.chanLabel)[c]
# Set some config based upon channel
if (cfg.getValue(ELMConfig.chanLabel)[c] in cfg.getValue(ELMConfig.chansToSkip)):
continue
if (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.BRIGHTFIELD):
minCircularity = 0.001 # We want to identify one big cell ball, so ignore small less circular objects
if cfg.params[ELMConfig.imgType] == "png":
minSize = 5;
else:
minSize = 500
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.BLUE) \
or (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.RED) \
or (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.GREEN): #
minCircularity = 0.001
minSize = 5
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.YELLOW):
minCircularity = 0.001
minSize = 5
# Process images in Z stack
for z in range(0, cfg.getValue(ELMConfig.numZ)):
zStr = cfg.getZStr(z);
for t in range(0, cfg.getValue(ELMConfig.numT)):
tStr = cfg.getTStr(t)
if (cfg.getValue(ELMConfig.imgType) == "png"):
# Brightfield uses the whole iamge
if (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.BRIGHTFIELD):
currIP = IJ.openImage(images[c][z][t][0])
else: # otherwise, we'll plit off channels
chanIdx = 2
if (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.RED):
chanIdx = 0
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.GREEN):
chanIdx = 1;
img = IJ.openImage(images[c][z][t][0])
imgChanns = ChannelSplitter.split(img);
img.close()
currIP = imgChanns[chanIdx];
else:
currIP = IJ.openImage(images[c][z][t][0])
resultsImage = currIP.duplicate()
dbgOutDesc = wellName + "_" + zStr + "_" + chanStr + "_" + tStr
if (cfg.getValue(ELMConfig.numT) > 1):
outputPath = os.path.join(wellPath, "images")
if not os.path.exists(outputPath):
os.makedirs(outputPath)
else:
outputPath = wellPath
if cfg.getValue(ELMConfig.debugOutput):
WindowManager.setTempCurrentImage(currIP)
IJ.saveAs('png', os.path.join(outputPath, "Orig_" + dbgOutDesc + ".png"))
# We need to get to a grayscale image, which will be done differently for different channels
startTime = time.time()
currIP = ELMImageUtils.getThresholdedMask(currIP, c, z, t, chanName, cfg, outputPath, dbgOutDesc)
endTime = time.time()
if not 'grayscale' in times:
times['grayscale'] = []
times['grayscale'].append(endTime-startTime)
if (not currIP):
resultsImage.close()
stats[c][z][t][ELMConfig.UM_AREA] = []
continue
startTime = time.time()
# Create a table to store the results
table = ResultsTable()
# Create a hidden ROI manager, to store a ROI for each blob or cell
#roim = RoiManager(True)
# Create a ParticleAnalyzer
measurements = Measurements.AREA + Measurements.MEAN + Measurements.STD_DEV + Measurements.MIN_MAX + Measurements.CENTROID + Measurements.RECT + Measurements.ELLIPSE
paFlags = ParticleAnalyzer.IN_SITU_SHOW | ParticleAnalyzer.SHOW_MASKS | ParticleAnalyzer.CLEAR_WORKSHEET
pa = ParticleAnalyzer(paFlags, measurements, table, minSize, Double.POSITIVE_INFINITY, minCircularity, 1.0)
#pa.setHideOutputImage(True)
# The Result image is copied when CurrIP can still have calibration from loading
# We want the output to be in terms of pixels, for ease of use, so adjust calibration
resultsImage.setCalibration(currIP.getCalibration())
Analyzer.setRedirectImage(resultsImage)
if not pa.analyze(currIP):
print "There was a problem in analyzing", currIP
endTime = time.time()
if not 'pa' in times:
times['pa'] = []
times['pa'].append(endTime-startTime)
#for i in range(0, roim.getCount()) :
# r = roim.getRoi(i);
# r.setColor(Color.red)
# r.setStrokeWidth(2)
# The measured areas are listed in the first column of the results table, as a float array:
newAreas = []
maxArea = 0
if table.getColumn(ResultsTable.AREA):
for pixArea in table.getColumn(ResultsTable.AREA):
a = pixArea * cfg.getValue(ELMConfig.pixelHeight) * cfg.getValue(ELMConfig.pixelWidth)
newAreas.append(a)
if (a > maxArea):
maxArea = a
# Threshold areas
idxToRemove = set()
if cfg.hasValue(ELMConfig.areaMaxPercentThreshold):
areaPercentThresh = cfg.getValue(ELMConfig.areaMaxPercentThreshold)
for i in range(0,len(newAreas)):
if newAreas[i] < (areaPercentThresh * maxArea):
idxToRemove.add(i)
if cfg.hasValue(ELMConfig.areaAbsoluteThreshold):
areaAbsoluteThresh = cfg.getValue(ELMConfig.areaAbsoluteThreshold)
for i in range(0,len(newAreas)):
if newAreas[i] < areaAbsoluteThresh:
idxToRemove.add(i)
for i in sorted(idxToRemove, reverse=True):
del newAreas[i]
stats[c][z][t][ELMConfig.UM_AREA] = newAreas
centroidX = []
centroidY = []
roiX = []
roiY = []
roiWidth = []
roiHeight = []
rArea = []
# Store all of the other data
for col in range(0,table.getLastColumn()):
newData = table.getColumn(col)
if not newData is None:
if col == ResultsTable.X_CENTROID:
for idx in idxToRemove:
centroidX.append(newData[idx])
if col == ResultsTable.Y_CENTROID:
for idx in idxToRemove:
centroidY.append(newData[idx])
if col == ResultsTable.ROI_X:
for idx in idxToRemove:
roiX.append(int(newData[idx]))
if col == ResultsTable.ROI_Y:
for idx in idxToRemove:
roiY.append(int(newData[idx]))
if col == ResultsTable.ROI_WIDTH:
for idx in idxToRemove:
roiWidth.append(int(newData[idx]))
if col == ResultsTable.ROI_HEIGHT:
for idx in idxToRemove:
roiHeight.append(int(newData[idx]))
if col == ResultsTable.AREA:
for idx in idxToRemove:
rArea.append(newData[idx])
for i in sorted(idxToRemove, reverse=True):
del newData[i]
stats[c][z][t][table.getColumnHeading(col)] = newData
IJ.saveAs('png', os.path.join(outputPath, "PreFiltered_Segmentation_" + dbgOutDesc + "_particles.png"))
# Remove the segmentation masks for the objects removed
currProcessor = currIP.getProcessor()
ff = FloodFiller(currProcessor)
currIP.getProcessor().setValue(0)
calib = resultsImage.getCalibration()
sortedAreaIndices = [i[0] for i in sorted(enumerate(rArea), key=lambda x:x[1])]
for idx in range(0, len(sortedAreaIndices)):
i = sortedAreaIndices[idx]
centX = int(calib.getRawX(centroidX[i]))
centY = int(calib.getRawY(centroidY[i]))
# Since the centroid isn't guaranteed to be part of the blob
# search around until an active pixel is found
found = False
halfWidth = min([roiHeight[i], roiWidth[i]])
for offset in range(0,halfWidth):
if found:
break
for x in range(centX-offset,centX+offset+1):
if found:
break
for y in range(centY-offset,centY+offset+1):
if not currProcessor.getPixel(x,y) == 0x0:
found = True
finalX = x
finalY = y
break
if not found:
print "\t\tZ = " + str(z) + ", T = " + str(t) + ", chan " + chanName + ": ERROR: Never found active pixel for filtered blob, centroid: " + str(centX) + ", " + str(centY)
else:
currProcessor.setRoi(roiX[i], roiY[i], roiWidth[i], roiHeight[i])
ff.fill8(finalX,finalY)
#IJ.saveAs('png', os.path.join(outputPath, "Segmentation_" + dbgOutDesc + "_" + str(idx) + ".png"))
#outImg = pa.getOutputImage()
IJ.saveAs('png', os.path.join(outputPath, "Segmentation_" + dbgOutDesc + "_particles.png"))
if cfg.hasValue(ELMConfig.createSegMask) and cfg.getValue(ELMConfig.createSegMask) == True:
# Create segmentation mask
segMask = currIP.duplicate()
segMask.setTitle("SegMask_" + dbgOutDesc)
# Iterate by smallest area first
# We are more likely to correctly label small areas
if len(newAreas) > 0:
segProcessor = segMask.getProcessor()
if (len(newAreas) > 255):
segProcessor = segProcessor.convertToShort(True)
segMask.setProcessor(segProcessor)
ff = FloodFiller(segProcessor)
sortedAreaIndices = [i[0] for i in sorted(enumerate(stats[c][z][t]['Area']), key=lambda x:x[1])]
for idx in range(0, len(sortedAreaIndices)):
row = sortedAreaIndices[idx]
centX = int(stats[c][z][t]['X'][row])
centY = int(stats[c][z][t]['Y'][row])
roiX = int(stats[c][z][t]['BX'][row])
roiY = int(stats[c][z][t]['BY'][row])
roiWidth = int(stats[c][z][t]['Width'][row])
roiHeight = int(stats[c][z][t]['Height'][row])
area = stats[c][z][t]['Area'][row]
halfRoiHeight = roiHeight/2 + 1
halfRoiWidth = roiWidth/2 + 1
# Since the centroid isn't guaranteed to be part of the blob
# search around until an active pixel is found
found = False
for xOffset in range(0,halfRoiWidth):
if found:
break
for yOffset in range(0, halfRoiHeight):
if found:
break
for x in range(centX-xOffset,centX+xOffset+1):
if found:
break
for y in range(centY-yOffset,centY+yOffset+1):
# original image and this image for masked pixel
# By checking original image, we avoid confusion with a label of 255
if segProcessor.getPixel(x,y) == 255 and currProcessor.getPixel(x,y) == 255:
found = True
finalX = x
finalY = y
break
if not found:
print "\t\tZ = " + str(z) + ", T = " + str(t) + ", chan " + chanName + ": ERROR: Never found active pixel for seg mask, centroid, roi, area (px): " \
+ str(centX) + ", " + str(centY) + ", " + str(roiX) + ", " + str(roiY) + ", " + str(roiWidth) + ", " + str(roiHeight) + ", " + str(area)
else:
segProcessor.setRoi(roiX, roiY, roiWidth, roiHeight)
segProcessor.setColor(row + 1)
ff.fill8(finalX,finalY)
lut = LutLoader.openLut(cfg.getValue(ELMConfig.lutPath))
segMask.setLut(lut)
WindowManager.setTempCurrentImage(segMask);
IJ.saveAs('png', os.path.join(outputPath, "SegMask_" + dbgOutDesc + "_particles.png"))
startTime = time.time()
width = currIP.getWidth();
height = currIP.getHeight();
overlayImage = resultsImage.duplicate()
overlayImage.setTitle("Overlay_" + dbgOutDesc + "_particles")
if not overlayImage.getType() == ImagePlus.COLOR_RGB:
imgConvert = ImageConverter(overlayImage)
imgConvert.convertToRGB()
overlayProcessor = overlayImage.getProcessor()
currProcessor = currIP.getProcessor()
if (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.BRIGHTFIELD):
maskColor = 0x0000ff00
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.YELLOW):
maskColor = 0x000000ff
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.RED):
maskColor = 0x0000ff00
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.GREEN):
maskColor = 0x00ff0000
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.BLUE):
maskColor = 0x00ffff00
for x in range(0, width):
for y in range(0,height):
currPix = currProcessor.getPixel(x,y);
if not currPix == 0x00000000:
overlayProcessor.putPixel(x, y, maskColor)
endTime = time.time()
if not 'overlay' in times:
times['overlay'] = []
times['overlay'].append(endTime-startTime)
WindowManager.setTempCurrentImage(overlayImage);
IJ.saveAs('png', os.path.join(outputPath, "Overlay_" + dbgOutDesc + "_particles.png"))
#currIP.hide()
currIP.close()
resultsImage.close()
timesAvg = {}
for key in times:
timeList = times[key]
timesAvg[key] = sum(timeList) / len(timeList);
print("processImage times " + str(timesAvg))
return stats
from java.lang import Double
imp = IJ.getImage()
# Create a table to store the results
table = ResultsTable()
# Create a hidden ROI manager, to store a ROI for each blob or cell
roim = RoiManager(True)
# Create a ParticleAnalyzer, with arguments:
# 1. options (could be SHOW_ROI_MASKS, SHOW_OUTLINES, SHOW_MASKS, SHOW_NONE, ADD_TO_MANAGER, and others; combined with bitwise-or)
# 2. measurement options (see [http://rsb.info.nih.gov/ij/developer/api/ij/measure/Measurements.html Measurements])
# 3. a ResultsTable to store the measurements
# 4. The minimum size of a particle to consider for measurement
# 5. The maximum size (idem)
# 6. The minimum circularity of a particle
# 7. The maximum circularity
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA
+ Measurements.CENTER_OF_MASS
+ Measurements.SHAPE_DESCRIPTORS
+ Measurements.INTEGRATED_DENSITY,
table, 0,
Double.POSITIVE_INFINITY, 0.0, 1.0)
pa.setHideOutputImage(True)
if pa.analyze(imp):
print "All ok"
table.show("foooo");
else:
print "There was a problem in analyzing", blobs
print(dupes)
for dupe in dupes:
# get centroidys
rt = ResultsTable();
#rm = RoiManager.getInstance();
#print(rm)
rm = RoiManager(False);
pa = ParticleAnalyzer((ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.SHOW_MASKS), (Measurements.CENTROID | Measurements.STACK_POSITION), rt, 500, 30000, 0.0, 1.0)
pa.setHideOutputImage(False)
keep_rois = [];
pa.analyze(imp);
IJ.run("Set Measurements...", "centroid redirect=None decimal=3");
frames = imp.getNFrames();
for fridx in range(0, frames):
rt.reset();
imp.setSliceWithoutUpdate(fridx + 1);
ip = imp.getProcessor();
if not pa.analyze(imp, ip):
raise Exception("something went wrong analysing particles!")
rt.show("centroids");
rm = RoiManager.getInstance();
if rm.getCount() > 0:
rois = rm.getRoisAsArray();
centroidsx = rt.getColumn(rt.getColumnIndex('X'));
centroidsy = rt.getColumn(rt.getColumnIndex('Y'));
#Threshold and watershed
IJ.run(channel, "Convert to Mask", "")
IJ.run(channel, "Watershed", "")
IJ.run("Set Measurements...", "area mean limit display redirect=None decimal=1");
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
#Analyses particles: finds all the objects that match criteria
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES, Measurements.AREA, table, minimum_size[i], maximum_size[i], 0.1, 1.0)
pa.setHideOutputImage(True)
pa.analyze(channel)
if thresholdMode:
happy=False
while(happy==False):
IJ.run("Threshold...")
WaitForUserDialog("Title", "Adjust threshold for " + color[i]).show()
summary[color[i] + "-threshold-used"] = ImageProcessor.getMinThreshold(channel.getProcessor())
channel.show()
summary[color[i] + "-threshold-used"] = ImageProcessor.getMinThreshold(channel.getProcessor())
copy=channel.duplicate()
copy.show()
if not rm:
rm = RoiManager()
else:
rm.reset()
#IJ.run(imp,"Analyze Particles...", "size=112-Infinity exclude add")
rt=ResultsTable()
pa=ParticleAnalyzer(EXCLUDE_EDGE_PARTICLES | ADD_TO_MANAGER,
0,
rt,
112/4,
200,
0.0,
1.0)
pa.analyze(imp)
# time.sleep(2)
print 'Size of results: ',rt.size()
# rm.runCommand("select","all")
# rm.runCommand("Fill","3")
save_path=saving_dir+"\\mask_%s" % (image[image.rindex('\\')+1:])
# print(save_path)
impMask = IJ.createImage("Mask", "8-bit grayscale-mode", imp.getWidth(), imp.getHeight(), imp.getNChannels(), imp.getNSlices(), imp.getNFrames())
impMask.show()
IJ.setForegroundColor(255, 255, 255)
rm.runCommand(impMask,"Deselect")
rm.runCommand(impMask,"Draw")
if doFileSave and FileSaver(impMask).saveAsTiff(save_path):
def __calRois(self, imp, indice) :
"""
Returns the ROIs of a slice given (identified with its n°) in a stack
"""
##imp=self.__dictImages[nameimages] # IL FAUT RÉCUPÉRER L'IMAGE DU STACK !!!!!
#if self.__batch : imp.hide()
#else : imp.show()
#imp.hide()
imp.show()
if self.__batch : imp.hide()
imp.setSlice(indice)
imp.killRoi()
ip = imp.getProcessor()
bs=BackgroundSubtracter()
#if str(self.__subback) == "0" or str(self.__subback) == "1" : self.__subback = bool(int(self.__subback))
#if self.__subback == True : IJ.run(imp, "Subtract Background...", "rolling="+str(self.__radius)+" light")
if self.__subback == True : bs.rollingBallBackground(ip, self.__radius, False, True, False, True, False)
if self.__runmacro :
imp.show()
imp.setSlice(indice)
imp.updateAndDraw()
IJ.runMacroFile(self.__macropath, imp.getTitle())
imp.updateAndDraw()
#if str(self.__manthresh) == "0" or str(self.__manthresh) == "1" : self.__manthresh = bool(int(self.__manthresh))
#if self.__manthresh : IJ.setThreshold(imp, self.__minthr, self.__maxthr)
if self.__manthresh :
ip.setThreshold(self.__minthr, self.__maxthr, ImageProcessor.RED_LUT)
else : self.__setThreshold(imp, indice)
rt=ResultsTable()
pa1=ParticleAnalyzer(ParticleAnalyzer.SHOW_MASKS+ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES , Measurements.AREA, rt, self.__minArea, self.__maxArea, self.__minCirc, self.__maxCirc)
pa1.setHideOutputImage(True)
pa1.analyze(imp)
masks=pa1.getOutputImage()
masks.getProcessor().erode()
masks.getProcessor().dilate()
masks.getProcessor().invertLut()
masks.getProcessor().threshold(1)
rm = RoiManager.getInstance()
if (rm==None): rm = RoiManager()
rm.runCommand("reset")
#rm.hide()
pa2=ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER+ParticleAnalyzer.CLEAR_WORKSHEET+ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES , Measurements.AREA, rt, self.__minArea, self.__maxArea, self.__minCirc, self.__maxCirc)
pa2.analyze(masks)
masks.close()
temparray=rm.getRoisAsArray()
for r in temparray :
tempnameroi=r.getName()
r.setPosition(indice)
r.setName(str(indice)+"-"+tempnameroi)
r.setStrokeWidth(1)
if len(self.__params) > 0 :
for k in self.__params:
#if k[0]=="Area": self.__minArea, self.__maxArea = str(k[1]), str(k[2])
if k[0]=="Area": self.__minArea, self.__maxArea = k[1], k[2]
for k in self.__params:
#if k[0]=="Circ": self.__minCirc, self.__maxCirc = str(k[1]), str(k[2])
if (k[0]=="Circ") and k[3] : self.__minCirc, self.__maxCirc = k[1], k[2]
else : self.__minCirc, self.__maxCirc = 0, 1
self.__rr.setRoisarray(temparray, imp)
self.__rr.setRange(indice, self.__params)
return self.__rr.includeRois
else : return temparray
def updatepressed(event):
self.__image=IJ.getImage()
rm = RoiManager.getInstance()
if (rm==None): rm = RoiManager()
rm.runCommand("reset")
self.__image.killRoi()
IJ.run("Threshold...")
IJ.setAutoThreshold(self.__image, "MaxEntropy")
rt=ResultsTable()
pa=ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER+ParticleAnalyzer.CLEAR_WORKSHEET , Measurements.AREA+Measurements.ELLIPSE+Measurements.MEAN, rt, 0.00, 10000.00, 0.00, 1.00)
pa.analyze(self.__image)
self.__roisArray=[]
self.__roisArray=rm.getRoisAsArray()
#for i in range(rm.getCount()) :
# rm.select(i)
# rm.runCommand("Set Color", "0000FF", 2)
IJ.resetThreshold(self.__image)
rt.show("tempRT")
areas=rt.getColumn(ResultsTable.AREA)
means=rt.getColumn(ResultsTable.MEAN)
majors=rt.getColumn(ResultsTable.MAJOR)
minors=rt.getColumn(ResultsTable.MINOR)
#print 0
if self.__slidersDict["Area_max"].getMaximum() < int(max(areas)+1):
# print 1
self.__slidersDict["Area_max"].setMaximum(int(max(areas))+1)
if self.__slidersDict["Area_min"].getMaximum() < int(max(areas)+1):
# print 2
self.__slidersDict["Area_min"].setMaximum(int(max(areas))+1)
if self.__slidersDict["Mean_max"].getMaximum() < int(max(means)+1):
# print 3
self.__slidersDict["Mean_max"].setMaximum(int(max(means))+1)
if self.__slidersDict["Mean_min"].getMaximum() < int(max(means)+1):
# print 4
self.__slidersDict["Mean_min"].setMaximum(int(max(means))+1)
if self.__slidersDict["Major_max"].getMaximum() < int(max(majors)):
# print 5
self.__slidersDict["Major_max"].setMaximum(int(max(majors))+1)
if self.__slidersDict["Major_min"].getMaximum() < int(max(majors)+1):
# print 6
self.__slidersDict["Major_min"].setMaximum(int(max(majors))+1)
if self.__slidersDict["Minor_max"].getMaximum() < int(max(minors)+1):
# print 7
self.__slidersDict["Minor_max"].setMaximum(int(max(minors))+1)
if self.__slidersDict["Minor_min"].getMaximum() < int(max(minors)+1):
# print 8
self.__slidersDict["Minor_min"].setMaximum(int(max(minors))+1)
if self.__slidersDict["AR_max"].getMaximum() < int((max(majors)+1)/min(minors)+1):
# print 9
self.__slidersDict["AR_max"].setMaximum(int((max(majors)+1)/(min(minors))))
if self.__slidersDict["AR_min"].getMaximum() < int((max(majors)+1)/min(minors)):
# print 10
self.__slidersDict["AR_min"].setMaximum(int((max(majors)+1)/(min(minors))))
#print 11
for sb in self.__slidersDict.values():
sb.repaint()
#rm.runCommand("reset")
#temprois=self.getIncludeRois()
#IJ.run(self.__image, "Remove Overlay", "")
#o=Overlay()
#for roi in temprois:
# o.addElement(roi)
#self.__image.killRoi()
#self.__image.setOverlay(o)
self.__image.updateAndDraw()
imp = IJ.openImage(binimgpath)
# paOpt = PA.CLEAR_WORKSHEET +\
paOpt = PA.SHOW_OUTLINES + PA.EXCLUDE_EDGE_PARTICLES # +\
# PA.INCLUDE_HOLES #+ \
# PA.SHOW_RESULTS
measOpt = PA.AREA + PA.CENTROID + PA.SLICE # + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY
rt = ResultsTable()
MINSIZE = 2
MAXSIZE = 10000
pa = PA(paOpt, measOpt, rt, MINSIZE, MAXSIZE)
pa.setHideOutputImage(True)
# pa.processStack = True
for i in range(imp.getStackSize()):
imp.setSlice(i + 1)
pa.analyze(imp)
# pa.getOutputImage().show()
rt.show("cells")
# rt = ResultsTable.open2(path)
dotlinker = DotLinker(loadmethod, rt) # better there is a constructor also with linkkost function object.
dotlinker.setTrajectoryThreshold(5)
dotlinker.setShowTrackTable(False)
# dotlinker = DotLinker(loadmethod)
linkcostfunction = dotlinker.setLinkCostFunction(lcAD)
linkcostfunction.setParameters(5.0, 2.0)
rtout = dotlinker.doLinking(False)
rtout.show("Tracks")
vd = ViewDynamicsArea(imp)
summary[color[i] + "-threshold-used"] = ImageProcessor.getMinThreshold(channel.getProcessor())
#Threshold and watershed
IJ.run(channel, "Convert to Mask", "")
IJ.run(channel, "Watershed", "")
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
#Analyses particles: finds all the objects that match criteria
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES, Measurements.AREA, table, minimum_size, maximum_size, 0.1, 1.0)
pa.setHideOutputImage(True)
pa.analyze(channel)
if thresholdMode:
channel.show()
WaitForUserDialog("Title", "Look at threshold for" + color[i]).show()
#adds count to summary
if table.getColumnIndex("Area") != -1:
summary[color[i] + "-ROI-count"] = len(table.getColumn(table.getColumnIndex("Area")))
channel.changes = False
channel.close()
def generate_background_rois(input_mask_imp,
params,
membrane_edges,
dilations=5,
threshold_method=None,
membrane_imp=None):
"""automatically identify background region based on auto-thresholded image, existing membrane edges and position of midpoint anchor"""
if input_mask_imp is None and membrane_imp is not None:
segmentation_imp = Duplicator().run(membrane_imp)
# do thresholding using either previous method if threhsold_method is None or using (less conservative?) threshold method
if (threshold_method is None
or not (threshold_method in params.listThresholdMethods())):
mask_imp = make_and_clean_binary(segmentation_imp,
params.threshold_method)
else:
mask_imp = make_and_clean_binary(segmentation_imp,
threshold_method)
segmentation_imp.close()
else:
input_mask_imp.killRoi()
mask_imp = Duplicator().run(input_mask_imp)
rois = []
IJ.setForegroundColor(0, 0, 0)
roim = RoiManager(True)
rt = ResultsTable()
for fridx in range(mask_imp.getNFrames()):
mask_imp.setT(fridx + 1)
# add extra bit to binary mask from loaded membrane in case user refined edges...
# flip midpoint anchor across the line joining the two extremes of the membrane,
# and fill in the triangle made by this new point and those extremes
poly = membrane_edges[fridx].getPolygon()
l1 = (poly.xpoints[0], poly.ypoints[0])
l2 = (poly.xpoints[-1], poly.ypoints[-1])
M = (0.5 * (l1[0] + l2[0]), 0.5 * (l1[1] + l2[1]))
Mp1 = (params.manual_anchor_midpoint[0][0] - M[0],
params.manual_anchor_midpoint[0][1] - M[1])
p2 = (M[0] - Mp1[0], M[1] - Mp1[1])
new_poly_x = list(poly.xpoints)
new_poly_x.append(p2[0])
new_poly_y = list(poly.ypoints)
new_poly_y.append(p2[1])
mask_imp.setRoi(PolygonRoi(new_poly_x, new_poly_y, PolygonRoi.POLYGON))
IJ.run(mask_imp, "Fill", "slice")
mask_imp.killRoi()
# now dilate the masked image and identify the unmasked region closest to the midpoint anchor
ip = mask_imp.getProcessor()
dilations = 5
for d in range(dilations):
ip.dilate()
ip.invert()
mask_imp.setProcessor(ip)
mxsz = mask_imp.getWidth() * mask_imp.getHeight()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.SHOW_PROGRESS,
ParticleAnalyzer.CENTROID, rt, 0, mxsz)
pa.setRoiManager(roim)
pa.analyze(mask_imp)
ds_to_anchor = [
math.sqrt((x - params.manual_anchor_midpoint[0][0])**2 +
(y - params.manual_anchor_midpoint[0][1])**2)
for x, y in zip(
rt.getColumn(rt.getColumnIndex("X")).tolist(),
rt.getColumn(rt.getColumnIndex("Y")).tolist())
]
if len(ds_to_anchor) > 0:
roi = roim.getRoi(ds_to_anchor.index(min(ds_to_anchor)))
rois.append(roi)
else:
rois.append(None)
roim.reset()
rt.reset()
roim.close()
mask_imp.close()
return rois
def analyze_homogeneity(image_title):
IJ.selectWindow(image_title)
raw_imp = IJ.getImage()
IJ.run(raw_imp, "Duplicate...", "title=Homogeneity duplicate")
IJ.selectWindow('Homogeneity')
hg_imp = IJ.getImage()
# Get a 2D image
if hg_imp.getNSlices() > 1:
IJ.run(hg_imp, "Z Project...", "projection=[Average Intensity]")
hg_imp.close()
IJ.selectWindow('MAX_Homogeneity')
hg_imp = IJ.getImage()
hg_imp.setTitle('Homogeneity')
# Blur and BG correct the image
IJ.run(hg_imp, 'Gaussian Blur...', 'sigma=' + str(HOMOGENEITY_RADIUS) + ' stack')
# Detect the spots
IJ.setAutoThreshold(hg_imp, HOMOGENEITY_THRESHOLD + " dark")
rm = RoiManager(True)
table = ResultsTable()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES,
Measurements.AREA, # measurements
table, # Output table
0, # MinSize
500, # MaxSize
0.0, # minCirc
1.0) # maxCirc
pa.setHideOutputImage(True)
pa.analyze(hg_imp)
areas = table.getColumn(table.getHeadings().index('Area'))
median_areas = compute_median(areas)
st_dev_areas = compute_std_dev(areas, median_areas)
thresholds_areas = (median_areas - (2 * st_dev_areas), median_areas + (2 * st_dev_areas))
roi_measurements = {'integrated_density': [],
'max': [],
'area': []}
IJ.setForegroundColor(0, 0, 0)
for roi in rm.getRoisAsArray():
hg_imp.setRoi(roi)
if REMOVE_CROSS and hg_imp.getStatistics().AREA > thresholds_areas[1]:
rm.runCommand('Fill')
else:
roi_measurements['integrated_density'].append(hg_imp.getStatistics().INTEGRATED_DENSITY)
roi_measurements['max'].append(hg_imp.getStatistics().MIN_MAX)
roi_measurements['integrated_densities'].append(hg_imp.getStatistics().AREA)
rm.runCommand('Delete')
measuremnts = {'mean_integrated_density': compute_mean(roi_measurements['integrated_density']),
'median_integrated_density': compute_median(roi_measurements['integrated_density']),
'std_dev_integrated_density': compute_std_dev(roi_measurements['integrated_density']),
'mean_max': compute_mean(roi_measurements['max']),
'median_max': compute_median(roi_measurements['max']),
'std_dev_max': compute_std_dev(roi_measurements['max']),
'mean_area': compute_mean(roi_measurements['max']),
'median_area': compute_median(roi_measurements['max']),
'std_dev_area': compute_std_dev(roi_measurements['max']),
}
# generate homogeinity image
# calculate interpoint distance in pixels
nr_point_columns = int(sqrt(len(measuremnts['mean_max'])))
# TODO: This is a rough estimation that does not take into account margins or rectangular FOVs
inter_point_dist = hg_imp.getWidth() / nr_point_columns
IJ.run(hg_imp, "Maximum...", "radius="+(inter_point_dist*1.22))
# Normalize to 100
IJ.run(hg_imp, "Divide...", "value=" + max(roi_measurements['max'] / 100))
IJ.run(hg_imp, "Gaussian Blur...", "sigma=" + (inter_point_dist/2))
hg_imp.getProcessor.setMinAndMax(0, 255)
# Create a LUT based on a predefined threshold
red = zeros(256, 'b')
green = zeros(256, 'b')
blue = zeros(256, 'b')
acceptance_threshold = HOMOGENEITY_ACCEPTANCE_THRESHOLD * 256 / 100
for i in range(256):
red[i] = (i - acceptance_threshold)
green[i] = (i)
homogeneity_LUT = LUT(red, green, blue)
hg_imp.setLut(homogeneity_LUT)
return hg_imp, measuremnts
def nucleusSegmentation(imp2):
""" Segmentation of nucleus image.
Nucleus are selected that:
1. No overlapping with dilated regions
2. close to circular shape. Deformed nuclei are rejected.
Outputs a binary image.
"""
#Convert to 8bit
ImageConverter(imp2).convertToGray8()
#blur slightly using Gaussian Blur
radius = 2.0
accuracy = 0.01
GaussianBlur().blurGaussian( imp2.getProcessor(), radius, radius, accuracy)
# Auto Local Thresholding
imps = ALT().exec(imp2, "Bernsen", 15, 0, 0, True)
imp2 = imps[0]
#ParticleAnalysis 0: prefiltering by size and circularity
rt = ResultsTable()
paOpt = PA.CLEAR_WORKSHEET +\
PA.SHOW_MASKS +\
PA.EXCLUDE_EDGE_PARTICLES +\
PA.INCLUDE_HOLES #+ \
# PA.SHOW_RESULTS
measOpt = PA.AREA + PA.STD_DEV + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY
MINSIZE = 20
MAXSIZE = 10000
pa0 = PA(paOpt, measOpt, rt, MINSIZE, MAXSIZE, 0.8, 1.0)
pa0.setHideOutputImage(True)
pa0.analyze(imp2)
imp2 = pa0.getOutputImage() # Overwrite
imp2.getProcessor().invertLut()
#impNuc = imp2.duplicate() ## for the ring.
impNuc = Duplicator().run(imp2)
#Dilate the Nucleus Area
## this should be 40 pixels in Cihan's method, but should be smaller.
#for i in range(20):
# IJ.run(imp2, "Dilate", "")
rf = RankFilters()
rf.rank(imp2.getProcessor(), RIMSIZE, RankFilters.MAX)
#Particle Analysis 1: get distribution of sizes.
paOpt = PA.CLEAR_WORKSHEET +\
PA.SHOW_NONE +\
PA.EXCLUDE_EDGE_PARTICLES +\
PA.INCLUDE_HOLES #+ \
# PA.SHOW_RESULTS
measOpt = PA.AREA + PA.STD_DEV + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY
rt1 = ResultsTable()
MINSIZE = 20
MAXSIZE = 10000
pa = PA(paOpt, measOpt, rt1, MINSIZE, MAXSIZE)
pa.analyze(imp2)
#rt.show('after PA 1')
#particle Analysis 2: filter nucleus by size and circularity.
#print rt1.getHeadings()
if (rt1.getColumnIndex('Area') > -1):
q1, q3, outlier_offset = getOutlierBound(rt1)
else:
q1 = MINSIZE
q3 = MAXSIZE
outlier_offset = 0
print imp2.getTitle(), ": no Nucleus segmented,probably too many overlaps"
paOpt = PA.CLEAR_WORKSHEET +\
PA.SHOW_MASKS +\
PA.EXCLUDE_EDGE_PARTICLES +\
PA.INCLUDE_HOLES #+ \
# PA.SHOW_RESULTS
rt2 = ResultsTable()
#pa = PA(paOpt, measOpt, rt, q1-outlier_offset, q3+outlier_offset, circq1-circoutlier_offset, circq3+circoutlier_offset)
pa = PA(paOpt, measOpt, rt2, q1-outlier_offset, q3+outlier_offset, 0.8, 1.0)
pa.setHideOutputImage(True)
pa.analyze(imp2)
impDilatedNuc = pa.getOutputImage()
#filter original nucleus
filteredNuc = ImageCalculator().run("AND create", impDilatedNuc, impNuc)
return filteredNuc