def wsDtSegmentation(pmap,
pmin,
minMembraneSize,
minSegmentSize,
sigmaMinima,
sigmaWeights,
cleanCloseSeeds=True):
# get the thresholded pmap
binary = numpy.zeros_like(pmap, dtype=numpy.uint32)
binary[pmap >= pmin] = 1
# delete small CCs
labeled = vigra.analysis.labelVolumeWithBackground(binary)
skneuro.oversegmentation.sizeFilterSegInplace(labeled,
int(numpy.max(labeled)),
int(minMembraneSize),
checkAtBorder=True)
# use cleaned binary image as mask
mask = numpy.zeros_like(binary, dtype=numpy.float32)
mask[labeled > 0] = 1.
# perform signed dt on mask
dt = vigra.filters.distanceTransform3D(mask)
dtInv = vigra.filters.distanceTransform3D(mask, background=False)
dtInv[dtInv > 0] -= 1
dtSigned = dt.max() - dt + dtInv
dtSignedSmoothMinima = vigra.filters.gaussianSmoothing(
dtSigned, sigmaMinima)
dtSignedSmoothWeights = vigra.filters.gaussianSmoothing(
dtSigned, sigmaWeights)
seeds = vigra.analysis.localMinima3D(dtSignedSmoothMinima,
neighborhood=26,
allowAtBorder=True)
if cleanCloseSeeds:
seeds = nonMaximumSuppressionSeeds(volumeToListOfPoints(seeds), dt)
seeds = placePointsInVolumen(seeds, mask.shape).astype(numpy.uint32)
seedsLabeled = vigra.analysis.labelVolumeWithBackground(seeds)
segmentation = vigra.analysis.watershedsNew(dtSignedSmoothWeights,
seeds=seedsLabeled,
neighborhood=26)[0]
skneuro.oversegmentation.sizeFilterSegInplace(segmentation,
int(numpy.max(segmentation)),
int(minSegmentSize),
checkAtBorder=True)
segmentation = vigra.analysis.watershedsNew(dtSignedSmoothWeights,
seeds=segmentation,
neighborhood=26)[0]
return segmentation
def findBestSeedCloserThanMembrane(seeds, distances, distanceTrafo, membraneDistance):
""" finds the best seed of the given seeds, that is the seed with the highest value distance transformation."""
closeSeeds = distances <= membraneDistance
numpy.zeros_like(closeSeeds)
# iterate over all close seeds
maximumDistance = -numpy.inf
mostCentralSeed = None
for seed in seeds[closeSeeds]:
if distanceTrafo[seed[0], seed[1], seed[2]] > maximumDistance:
maximumDistance = distanceTrafo[seed[0], seed[1], seed[2]]
mostCentralSeed = seed
return mostCentralSeed
def findBestSeedCloserThanMembrane(seeds, distances, distanceTrafo,
membraneDistance):
""" finds the best seed of the given seeds, that is the seed with the highest value distance transformation."""
closeSeeds = distances <= membraneDistance
numpy.zeros_like(closeSeeds)
# iterate over all close seeds
maximumDistance = -numpy.inf
mostCentralSeed = None
for seed in seeds[closeSeeds]:
if distanceTrafo[seed[0], seed[1], seed[2]] > maximumDistance:
maximumDistance = distanceTrafo[seed[0], seed[1], seed[2]]
mostCentralSeed = seed
return mostCentralSeed
def wsDtSegmentation(pmap, pmin, minMembraneSize, minSegmentSize, sigmaMinima, sigmaWeights, cleanCloseSeeds=True):
# get the thresholded pmap
binary = numpy.zeros_like(pmap, dtype=numpy.uint32)
binary[pmap >= pmin] = 1
# delete small CCs
labeled = vigra.analysis.labelVolumeWithBackground(binary)
skneuro.oversegmentation.sizeFilterSegInplace(labeled, int(numpy.max(labeled)), int(minMembraneSize), checkAtBorder=True)
# use cleaned binary image as mask
mask = numpy.zeros_like(binary, dtype = numpy.float32)
mask[labeled > 0] = 1.
# perform signed dt on mask
dt = vigra.filters.distanceTransform3D(mask)
dtInv = vigra.filters.distanceTransform3D(mask, background=False)
dtInv[dtInv>0] -= 1
dtSigned = dt.max() - dt + dtInv
dtSignedSmoothMinima = vigra.filters.gaussianSmoothing(dtSigned, sigmaMinima)
dtSignedSmoothWeights = vigra.filters.gaussianSmoothing(dtSigned, sigmaWeights)
seeds = vigra.analysis.localMinima3D(dtSignedSmoothMinima, neighborhood=26, allowAtBorder=True)
if cleanCloseSeeds:
seeds = nonMaximumSuppressionSeeds(volumeToListOfPoints(seeds), dt)
seeds = placePointsInVolumen(seeds, mask.shape).astype(numpy.uint32)
seedsLabeled = vigra.analysis.labelVolumeWithBackground(seeds)
segmentation = vigra.analysis.watershedsNew(dtSignedSmoothWeights, seeds = seedsLabeled, neighborhood=26)[0]
skneuro.oversegmentation.sizeFilterSegInplace(segmentation, int(numpy.max(segmentation)), int(minSegmentSize), checkAtBorder=True)
segmentation = vigra.analysis.watershedsNew(dtSignedSmoothWeights, seeds = segmentation, neighborhood=26)[0]
return segmentation
