distMin = dist
return centPart, (x, y)
def isSpecialParticle(image, roim, iParticle, boundariesNuc, boundariesMito, pixelSize, z):
try:
image.setSlice(int(round(z)))
image2 = ImagePlus("slice",image.getProcessor())
roim.select(image2, iParticle)
image3 = Duplicator().run(image2);
image3.show()
sp = segment_Particles()
image3 = sp.preprocessImage(image3, "MAX", "C1", 0, 0)
image3 = sp.segmentParticles(image3, "Triangle", boundariesNuc[0], boundariesNuc[1], 0, 0)
if roim.getCount() > 0:
roim.select(image3, 0)
stats = image3.getStatistics(Measurements.AREA + Measurements.SHAPE_DESCRIPTORS)
IJ.log("Area (um2) " + str(stats.area*pixelSize*pixelSize) + "; Aspect Ratio " + str(stats.major/stats.minor))
if stats.area > boundariesMito[0] and stats.area < boundariesMito[1] and stats.major/stats.minor > boundariesMito[2]:
return 1
else:
return 0
except BaseException, err:
# analyse objects and put results in table
IJ.run("Set Measurements...", "area display redirect=None decimal=5")
IJ.run(imp, "Analyze Particles...", "exclude add")
manager = RoiManager.getRoiManager()
manager.runCommand("Save",
file.toString() + ".RoiSet.zip")
for roi in manager.getRoisAsArray():
copy.setRoi(roi)
copy.show()
IJ.run(copy, "Enlarge...", "enlarge=-2")
# analyse the image
statistics = copy.getStatistics()
print("mean: " + str(statistics.mean))
print("area:" + str(statistics.area))
# write the analysed valurs in a table
table = ResultsTable.getResultsTable()
table.incrementCounter()
table.addValue("filename", file.toString())
table.addValue("mean_robert", statistics.mean)
table.addValue("area", statistics.area)
table.addValue("total instensity",
statistics.mean * statistics.area)
table.show("Results")
# dirty hack to make the program stop here:
# x = 1 / 0
def processImage(filename):
# many zeiss images have a pyramid format. In order to speed up the evaluation, we chose only the second highest resolution (=series_2).
# depending on your computer/workstation this parameter can be optimized (i.e. chose series_1 if you have a fast computer or series_3 for slow ones)
IJ.run(
"Bio-Formats", "open=" + filename +
" color_mode=Composite rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT series_2"
)
imp = IJ.getImage()
imp.show()
if (not entertain):
imp.show()
# preparing memory for saving the results
original1 = NewImage.createFloatImage("Original 1",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
original1Map = original1.getProcessor()
original2 = NewImage.createFloatImage("Original 2",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
original2Map = original2.getProcessor()
spotCount1 = NewImage.createFloatImage("Spot count 1",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
spotCount1Map = spotCount1.getProcessor()
spotCount2 = NewImage.createFloatImage("Spot count 2",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
spotCount2Map = spotCount2.getProcessor()
ratio = NewImage.createFloatImage("Ratio",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
ratioMap = ratio.getProcessor()
if (entertain):
ratio.show()
# go through all tiles
for x in range(0, ratioMap.getWidth() - 1):
for y in range(0, ratioMap.getHeight() - 1):
# crop out the tile from the original images
imp.setRoi(Roi(x * tileSize, y * tileSize, tileSize, tileSize))
channel1 = Duplicator().run(imp, 1, 1, 1, 1, 1, 1)
channel2 = Duplicator().run(imp, 2, 2, 1, 1, 1, 1)
# spot detection
spots1 = detectSpots(channel1,
spotDetection_GaussianBlur_sigma_GFP,
spotDetection_findMaxima_noise_GFP)
spots2 = detectSpots(channel2,
spotDetection_GaussianBlur_sigma_DAPI,
spotDetection_findMaxima_noise_DAPI)
# pixel statistics
statistics1 = channel1.getStatistics()
statistics2 = channel2.getStatistics()
# calculate ratio if spots were found
s1 = 0
s2 = 0
r = 0
if (spots1 is not None and spots2 is not None):
fp1 = spots1.getFloatPolygon()
fp2 = spots2.getFloatPolygon()
s1 = fp1.npoints
s2 = fp2.npoints
if (s2 > cutOff_DAPI and s1 > cutOff_GFP):
r = 1.0 * s1 / s2
# fill result memory
original1Map.setf(x, y, statistics1.mean)
original2Map.setf(x, y, statistics2.mean)
spotCount1Map.setf(x, y, s1)
spotCount2Map.setf(x, y, s2)
ratioMap.setf(x, y, r)
if (entertain):
# show current result image
ratio.updateAndDraw()
IJ.run(ratio, "Enhance Contrast", "saturated=0.35")
# put all results image channels together to one image
images = []
images.append(original1)
images.append(original2)
images.append(spotCount1)
images.append(spotCount2)
images.append(ratio)
resultMap = RGBStackMerge.mergeChannels(images, False)
# fix pixel size
# factor is multiplied by 2 because ImageJ seems to have a problem when using .czi file series of lower resolution (i.e. series_2); please check for individual cases!
factor = (imp.getWidth() / resultMap.getWidth()) * 2
IJ.run(
resultMap, "Properties...", "channels=5 slices=1 frames=1 unit=" +
imp.getCalibration().getUnit() + " pixel_width=" +
str(imp.getCalibration().pixelWidth * factor) + " pixel_height=" +
str(imp.getCalibration().pixelHeight * factor) + " voxel_depth=1.0000")
IJ.run(
ratio, "Properties...", "channels=1 slices=1 frames=1 unit=" +
imp.getCalibration().getUnit() + " pixel_width=" +
str(imp.getCalibration().pixelWidth * factor) + " pixel_height=" +
str(imp.getCalibration().pixelHeight * factor) + " voxel_depth=1.0000")
# visualisation
resultMap.setC(1)
IJ.run(resultMap, "Green", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(2)
IJ.run(resultMap, "Blue", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(3)
IJ.run(resultMap, "mpl-inferno", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(4)
IJ.run(resultMap, "mpl-inferno", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(5)
IJ.run(resultMap, "mpl-inferno", "")
resultMap.show()
IJ.resetMinAndMax(resultMap)
resultMap.setDisplayMode(IJ.COLOR)
IJ.run(ratio, "mpl-inferno", "")
IJ.setMinAndMax(ratio, 0, 1)
# save result
IJ.saveAsTiff(resultMap, filename + "_suitable-name_map.tif")
IJ.saveAsTiff(ratio, filename + "_suitable-name_ratio.tif")
IJ.run("Close All", "")
