destDir = sys.argv[1] + "/"
# helper function to create the directories like mkdir -p
def mkdir( path ):
p = ""
for d in path.split("/") :
p += d + "/"
if not os.path.exists( p ):
os.mkdir( p )
def log( s ):
print s,
sys.stdout.flush()
# the pipeline used to convert the lsm files to the a serie of tif used in freed
reader = itk.lsm()
writer = itk.ImageSeriesWriter.IUC3IUC2.New(reader, UseCompression=True)
# the names must be generated with a NumericSeriesFileNames object, because the
# vector of strings are not usable yet from python
names = itk.NumericSeriesFileNames.New()
# make the slice number start at 0 instead of 1
names.SetStartIndex( 0 )
# the descriptor file contain a header...
descriptorHeaderTpl = """STACK "%(channelName)s";
IMAGESPATH "%(imagePath)s";
PIXWIDTH %(pixelWidth)s;
PIXHEIGHT %(pixelHeight)s;
RELPOSITION %(sliceSpacing)s;
"""
i.UpdateLargestPossibleRegion() i = itk.image(i) return i.GetNumberOfLabelObjects() def copyImage( f ): """Copy an itk.Image object. """ f.UpdateLargestPossibleRegion() i = f.GetOutput() imgDuplicator = itk.ImageDuplicator[i].New(i) imgDuplicator.Update() return imgDuplicator.GetOutput() readerNuclei = itk.lsm(channel=0, fileName=inputImageName) medianNuclei = itk.MedianImageFilter.IUC3IUC3.New(readerNuclei) gaussianNuclei = itk.SmoothingRecursiveGaussianImageFilter.IUC3IUC3.New(medianNuclei, Sigma=0.2) fillNuclei = itk.SliceBySliceImageFilter.IUC3IUC3.New(gaussianNuclei, Filter=fill.GetPointer()) sizeNuclei = itk.PhysicalSizeOpeningImageFilter.IUC3IUC3.New(fillNuclei, Lambda=200) gradientNuclei = itk.GradientMagnitudeRecursiveGaussianImageFilter.IUC3IUC3.New(sizeNuclei, Sigma=0.36) ratsNuclei = itk.RobustAutomaticThresholdImageFilter.IUC3IUC3IUC3.New(sizeNuclei, gradientNuclei) # enlarge the nuclei to be sure to found the spots on the border dilateNuclei = itk.BinaryDilateImageFilter.IUC3IUC3SE3.New(ratsNuclei, Kernel=itk.strel(3, [5,5,3])) # split and labelize the nuclei maurerNuclei = itk.SignedMaurerDistanceMapImageFilter.IUC3IF3.New(dilateNuclei, UseImageSpacing=True) watershedNuclei = itk.MorphologicalWatershedImageFilter.IF3IUC3.New(maurerNuclei, Level=5.0, MarkWatershedLine=False) #, FullyConnected=True) maskWatershedNuclei = itk.MaskImageFilter.IUC3IUC3IUC3.New(watershedNuclei, dilateNuclei) lmNuclei = itk.LabelImageToLabelMapFilter.IUC3LM3.New(maskWatershedNuclei) # the same with the real mask (not the enlarged one), and also compute shape attributes for that one
itk.MultiThreader.SetGlobalDefaultNumberOfThreads(1)
def med(numbers):
"Return the median of the list of numbers."
# Sort the list and take the middle element.
n = len(numbers)
copy = numbers[:] # So that "numbers" keeps its original order
copy.sort()
if n & 1: # There is an odd number of elements
return copy[n // 2]
else:
return (copy[n // 2 - 1] + copy[n // 2]) / 2
reader = itk.lsm(ImageType=itk.Image.US3)
# use a median to remove some noise
median = itk.MedianImageFilter.IUS3IUS3.New(reader)
# first find the beads, to process them one by one
# That's done by projecting the image on the z axis, splitting the 2D image in several
# regions (one per bead) and unprojecting the regions in the original 3d image
projz = itk.MaximumProjectionImageFilter.IUS3IUS2.New(median)
invert = itk.InvertIntensityImageFilter.IUS2IUS2.New(projz)
watershed = itk.MorphologicalWatershedImageFilter.IUS2IUS2.New(invert, MarkWatershedLine=False)
wrelabel = itk.RelabelComponentImageFilter.IUS2IUS2.New(watershed)
upperdim = itk.UpperDimensionImageFilter.IUS2IUS3.New(wrelabel, NewDimension=2)
# mask the image to select only one bead
selectedLabel = itk.BinaryThresholdImageFilter.IUS3IUS3.New(upperdim)
genesFile = sys.stdout
else:
genesFile = file( opts.genes, "a" )
if os.path.getsize( opts.genes ) == 0:
genesFile.write( '"stimulation" "img" "nucleus" "gene" "x" "y" "z" "px" "py" "pz" "dist" "ci" "mean" "max" "median" "nMean" "nMinimum" "nMaximum" "nMedian" "nSigma" "nKurtosis" "nSkewness"\n' )
# itk.auto_not_in_place()
##########################
# nuclei
##########################
readerNuclei = itk.lsm(channel=1)
# fill the holes - nucleoles etc - in the nuclei
fillHoles2D = itk.GrayscaleFillholeImageFilter.IUC2IUC2.New(auto_progress=False)
fillHolesNuclei = itk.SliceBySliceImageFilter.IUC3IUC3.New(readerNuclei, Filter=fillHoles2D.GetPointer())
# remove some noise and smooth the image
medianNuclei = itk.MedianImageFilter.IUC3IUC3.New(readerNuclei)
gaussianNuclei = itk.SmoothingRecursiveGaussianImageFilter.IUC3IUC3.New(medianNuclei, Sigma=0.2)
inputNuclei = gaussianNuclei
# to compare with different thresholding methods
otsuNuclei = itk.OtsuThresholdImageCalculator.IUC3.New(gaussianNuclei)
# now we have 2 things: a large shape used to find the spots, and a smaller one used to precisely find the
# border of the nucleus.
# use the rats algorithm to find the nucleus with precision
