transform4matrix = sys.argv[6]
transform4file = sys.argv[7]
#finalalign
transform5matrix = sys.argv[8]
originalpixelsize = float(sys.argv[9])
outxsize = int(sys.argv[10])
outysize = int(sys.argv[11])
outspacingx = float(sys.argv[12])
outspacingy = float(sys.argv[13])
outputfilename = sys.argv[14]
singlestartind = int(sys.argv[15])
singleendind = int(sys.argv[16])
listfilename = sys.argv[17]
(directorynamelist, filenamelist,
truenumberlist) = parseSlideNumbers.parse(listfilename, singlestartind,
singleendind, patientnumber)
# load the crop transform matrix
with open(transform2matrix) as f:
content = f.readlines()
content = [x.strip() for x in content]
cropmatrix = content[0].split(',')
# load the first transform file
with open(transform1matrix) as f:
content = f.readlines()
content = [x.strip() for x in content]
color_rev_lookup[(int(l[2]), int(l[3]), int(l[4]))] = l[1]
color_lookup[10000] = color_lookup[0]
try:
os.mkdir('data')
except OSError:
pass
def return_color(idx):
if idx == 10000:
return 0
res = color_lookup[idx]
ret = (res[1] << 16) + (res[2] << 8) + res[3]
return ret
# retrive list of nissl and fluro for comparsion using parseSlideNumbers.py
(nissldirectorylist, nisslnamelist, nissllist) = parseSlideNumbers.parse('BNBLists/M' + patientnumber + "_" + str.upper('N') + '_List.txt',singlestartind,singleendind,patientnumber)
#maxslicenumber = np.max((np.array(aavlist).max(),np.array(nissllist).max()))
filenamesfilt_nissl = list(nisslnamelist)
for idx in range(shape[1]):
print 'working on index', idx
#mm_per_px = 0.00368
mm_per_px = 0.00368 / 4
Coef = (Sx / mm_per_px, - Sy / mm_per_px)
offset_x = offset_y = 0
# try:
# os.mkdir('data/%s' % idx)
# except OSError:
# pass
def registration(patientnumber, singlestartind, singleendind,
listdirectoryname):
pixelsize = "80"
dspixelsize = "100"
costmetric = "MI"
atlasdirectoryname = "/sonas-hs/mitra/hpc/home/blee/data/atlas_images/"
targetdirectoryname = "/sonas-hs/mitra/hpc/home/blee/data/stackalign/" + patientnumber
outputdirectoryname = "/sonas-hs/mitra/hpc/home/blee/data/registration/" + patientnumber + '/fluoro_old4/'
matlabdirectoryname = "/opt/hpc/pkg/MATLAB/R2015b/bin/matlab"
candirectoryname = "/sonas-hs/mitra/hpc/home/blee/code/can/"
altslicedirectoryname = "/sonas-hs/mitra/hpc/home/blee/data/stackalign/"
registrationoutputdirectoryname = "/sonas-hs/mitra/hpc/home/blee/data/registration/" + patientnumber + "/"
dimension = 2
affine = sitk.AffineTransform(dimension)
identityAffine = list(affine.GetParameters())
identityDirection = list(affine.GetMatrix())
zeroOrigin = [0] * dimension
zeroIndex = [0] * dimension
# prepare output
if not os.path.isdir(outputdirectoryname):
os.mkdir(outputdirectoryname)
if not os.path.isdir(outputdirectoryname + patientnumber +
'_AAV_registered'):
os.mkdir(outputdirectoryname + patientnumber + '_AAV_registered')
if not os.path.isdir(outputdirectoryname + patientnumber +
'_AAV_registered_rigidtrans'):
os.mkdir(outputdirectoryname + patientnumber +
'_AAV_registered_rigidtrans')
# load the two images
aavImg = sitk.ReadImage(
targetdirectoryname + 'F_maskimg/' + patientnumber + '_' + pixelsize +
'_AAV_full_cropped.img', sitk.sitkFloat32)
#aavImg = sitk.ReadImage(registrationoutputdirectoryname + patientnumber + "_40_AAV_full_firstalign.img")
aavImg.SetDirection((1, 0, 0, 0, 1, 0, 0, 0, 1))
aavImg.SetOrigin((0, 0, 0))
#nisslImg = sitk.ReadImage(targetdirectoryname + '/' + patientnumber + '_' + pixelsize + '_full.img',sitk.sitkFloat32)
nisslImg = sitk.ReadImage(registrationoutputdirectoryname + patientnumber +
"_orig_target_STS_sectionsmissing.img")
#nisslImg = sitk.ReadImage(registrationoutputdirectoryname + patientnumber + "_orig_target_STS.img")
nisslImg.SetDirection((1, 0, 0, 0, 1, 0, 0, 0, 1))
nisslImg.SetOrigin((0, 0, 0))
# for each target, extract the Nissl slice index values and the Fluoro slice index values
'''
#nissllist = [None]*1000
#aavlist = [None]*1000
#aavnamelist = [None]*1000
#ka = 0
#kn = 0
#if patientnumber == "PMD2044" or patientnumber == "PMD2048" or patientnumber == "PMD2050" or patientnumber == "PMD2084":
# aavslicedirectoryname = "/cis/home/jpatel/Histology Registration (Partha)/Data/Original/" + patientnumber + "/REgisteredAAVs"
# nisslslicedirectoryname = "/cis/home/jpatel/Histology Registration (Partha)/Data/Original/" + patientnumber + "/REgisteredNissls"
# aavslicedirectory = os.listdir(aavslicedirectoryname)
# nisslslicedirectory = os.listdir(nisslslicedirectoryname)
# for f in aavslicedirectory:
# if f.find('.tif') != -1:
# uind = f.find('.tif')
aavlist[ka] = int(f[uind-4:uind])
aavnamelist[ka] = f[0:uind]
ka += 1
for f in nisslslicedirectory:
if f.find('.tif') != -1:
uind = f.find('.tif')
nissllist[kn] = int(f[uind-4:uind])
kn += 1
else:
slicedirectoryname = targetdirectoryname + '/10um_downsampled/'
if os.path.isdir(slicedirectoryname) and os.listdir(slicedirectoryname) != []:
slicedirectory = os.listdir(slicedirectoryname)
for f in slicedirectory:
if f.find('F') != -1:
uind = f.find('.jp2')
aavlist[ka] = int(f[uind-4:uind])
aavnamelist[ka] = f[0:uind]
ka += 1
elif f.find('N') != -1:
uind = f.find('.jp2')
nissllist[kn] = int(f[uind-4:uind])
kn += 1
else:
nisslslicedirectoryname = altslicedirectoryname + patientnumber + "N"
aavslicedirectoryname = altslicedirectoryname + patientnumber + "F"
aavslicedirectory = os.listdir(aavslicedirectoryname)
nisslslicedirectory = os.listdir(nisslslicedirectoryname)
for f in aavslicedirectory:
if f.find('.png') != -1:
uind = f.find('.png')
aavlist[ka] = int(f[uind-4:uind])
aavnamelist[ka] = f[0:uind]
ka += 1
for f in nisslslicedirectory:
if f.find('.png') != -1:
uind = f.find('.png')
nissllist[kn] = int(f[uind-4:uind])
kn += 1
#aavlist = filter(None, aavlist)
#aavnamelist = filter(None, aavnamelist)
#nissllist = filter(None, nissllist)
#aavidx = [i[0] for i in sorted(enumerate(aavlist), key=lambda x:x[1])]
#aavnamelist = [ aavnamelist[i] for i in aavidx ]
#aavlist.sort()
#nissllist.sort()
'''
# read list file into list
listfile = open(listdirectoryname + '/' + patientnumber + "_" +
str.upper('F') + '_List.txt')
(aavdirectorylist, aavnamelist, aavlist) = parseSlideNumbers.parse(
listdirectoryname + '/' + patientnumber + "_" + str.upper('F') +
'_List.txt', singlestartind, singleendind, patientnumber)
listfile = open(listdirectoryname + '/' + patientnumber + "_" +
str.upper('N') + '_List.txt')
(nissldirectorylist, nisslnamelist, nissllist) = parseSlideNumbers.parse(
listdirectoryname + '/' + patientnumber + "_" + str.upper('N') +
'_List.txt', singlestartind, singleendind, patientnumber)
print(aavlist)
kk = 0
# for each slice of the fluoro image, find the closest slice in the Nissl image
#for f in aavlist:
for i in range(len(aavlist)):
f = aavlist[i]
print('iter ' + str(i) + ', f = ' + str(f) + ', aavlist = ')
print(aavlist)
# I'm searching for this aav slice number plus 0.5 in case that nissl slice is missing but the one greater than it exists
closestnumber = min(nissllist, key=lambda x: abs(x - (f + 0.5)))
# extract those two slices
aavslice = aavImg[:, f - 1, :]
nisslslice = nisslImg[:, closestnumber - 1 + 40, :]
#perform translation registration
try:
interpolator = sitk.sitkLinear
transtransform = sitk.TranslationTransform(dimension)
registration = sitk.ImageRegistrationMethod()
registration.SetInterpolator(interpolator)
registration.SetInitialTransform(transtransform)
numHistogramBins = 64
registration.SetMetricAsMattesMutualInformation(numHistogramBins)
learningRate = 0.02
iterations = 2000
registration.SetOptimizerAsRegularStepGradientDescent(
learningRate=learningRate,
numberOfIterations=iterations,
estimateLearningRate=registration.EachIteration,
minStep=0.0005)
#registration.AddCommand(sitk.sitkIterationEvent, lambda: print("{0}.\t {1} \t{2}".format(registration.GetOptimizerIteration(),registration.GetMetricValue(), registration.GetOptimizerLearningRate())))
registration.Execute(
sitk.SmoothingRecursiveGaussian(nisslslice, 0.4),
sitk.SmoothingRecursiveGaussian(aavslice, 0.4))
translation = identityAffine[0:dimension**2] + list(
transtransform.GetOffset())
outImg = ndreg2D.imgApplyAffine2D(aavslice,
translation,
size=nisslslice.GetSize())
outImgnp = sitk.GetArrayFromImage(outImg)
lastimgnp = sitk.GetArrayFromImage(nisslslice)
except:
translation = [1, 0, 0, 1, 0, 0]
#perform translation registration
try:
interpolator = sitk.sitkLinear
transtransform = sitk.TranslationTransform(dimension)
transtransform.SetOffset(translation[4:6])
registration = sitk.ImageRegistrationMethod()
registration.SetInterpolator(interpolator)
registration.SetInitialTransform(transtransform)
numHistogramBins = 64
registration.SetMetricAsMattesMutualInformation(numHistogramBins)
learningRate = 0.005
iterations = 2000
registration.SetOptimizerAsRegularStepGradientDescent(
learningRate=learningRate,
numberOfIterations=iterations,
estimateLearningRate=registration.EachIteration,
minStep=0.0001)
#registration.AddCommand(sitk.sitkIterationEvent, lambda: print("{0}.\t {1} \t{2}".format(registration.GetOptimizerIteration(),registration.GetMetricValue(), registration.GetOptimizerLearningRate())))
registration.Execute(
sitk.SmoothingRecursiveGaussian(nisslslice, 0.35),
sitk.SmoothingRecursiveGaussian(aavslice, 0.35))
translation = identityAffine[0:dimension**2] + list(
transtransform.GetOffset())
outImg = ndreg2D.imgApplyAffine2D(aavslice,
translation,
size=nisslslice.GetSize())
outImgnp = sitk.GetArrayFromImage(outImg)
lastimgnp = sitk.GetArrayFromImage(nisslslice)
except:
translation = [1, 0, 0, 1, 0, 0]
# perform euler2d registration
try:
transform = sitk.Euler2DTransform()
transform.SetTranslation(translation[4:6])
registration = sitk.ImageRegistrationMethod()
registration.SetInterpolator(interpolator)
registration.SetInitialTransform(transform)
numHistogramBins = 64
registration.SetMetricAsMattesMutualInformation(numHistogramBins)
learningRate = 0.02
iterations = 2000
registration.SetOptimizerAsRegularStepGradientDescent(
learningRate=learningRate,
numberOfIterations=iterations,
estimateLearningRate=registration.EachIteration,
minStep=0.00005)
#registration.AddCommand(sitk.sitkIterationEvent, lambda: print("{0}.\t {1} \t{2}".format(registration.GetOptimizerIteration(),registration.GetMetricValue(), registration.GetOptimizerLearningRate())))
registration.Execute(
sitk.SmoothingRecursiveGaussian(nisslslice, 0.2),
sitk.SmoothingRecursiveGaussian(aavslice, 0.2))
euler2d = list(transform.GetMatrix()) + list(
transform.GetTranslation())
outImg = ndreg2D.imgApplyAffine2D(aavslice,
euler2d,
size=nisslslice.GetSize())
outImgnp = sitk.GetArrayFromImage(outImg)
lastimgnp = sitk.GetArrayFromImage(nisslslice)
except:
euler2d = [1, 0, 0, 1, 0, 0]
# perform euler2d registration
try:
transform = sitk.Euler2DTransform()
transform.SetTranslation(euler2d[4:6])
transform.SetMatrix(euler2d[0:4])
registration = sitk.ImageRegistrationMethod()
registration.SetInterpolator(interpolator)
registration.SetInitialTransform(transform)
numHistogramBins = 64
registration.SetMetricAsMattesMutualInformation(numHistogramBins)
learningRate = 0.005
iterations = 2000
registration.SetOptimizerAsRegularStepGradientDescent(
learningRate=learningRate,
numberOfIterations=iterations,
estimateLearningRate=registration.EachIteration,
minStep=0.000025)
#registration.AddCommand(sitk.sitkIterationEvent, lambda: print("{0}.\t {1} \t{2}".format(registration.GetOptimizerIteration(),registration.GetMetricValue(), registration.GetOptimizerLearningRate())))
registration.Execute(
sitk.SmoothingRecursiveGaussian(nisslslice, 0.06),
sitk.SmoothingRecursiveGaussian(aavslice, 0.06))
euler2d = list(transform.GetMatrix()) + list(
transform.GetTranslation())
outImg = ndreg2D.imgApplyAffine2D(aavslice,
euler2d,
size=nisslslice.GetSize())
outImgnp = sitk.GetArrayFromImage(outImg)
lastimgnp = sitk.GetArrayFromImage(nisslslice)
except:
euler2d = [1, 0, 0, 1, 0, 0]
# perform euler2d registration
try:
transform = sitk.Euler2DTransform()
transform.SetTranslation(euler2d[4:6])
transform.SetMatrix(euler2d[0:4])
registration = sitk.ImageRegistrationMethod()
registration.SetInterpolator(interpolator)
registration.SetInitialTransform(transform)
numHistogramBins = 64
registration.SetMetricAsMattesMutualInformation(numHistogramBins)
learningRate = 0.002
iterations = 2000
registration.SetOptimizerAsRegularStepGradientDescent(
learningRate=learningRate,
numberOfIterations=iterations,
estimateLearningRate=registration.EachIteration,
minStep=0.000025)
#registration.AddCommand(sitk.sitkIterationEvent, lambda: print("{0}.\t {1} \t{2}".format(registration.GetOptimizerIteration(),registration.GetMetricValue(), registration.GetOptimizerLearningRate())))
registration.Execute(
sitk.SmoothingRecursiveGaussian(nisslslice, 0.04),
sitk.SmoothingRecursiveGaussian(aavslice, 0.04))
euler2d = list(transform.GetMatrix()) + list(
transform.GetTranslation())
outImg = ndreg2D.imgApplyAffine2D(aavslice,
euler2d,
size=nisslslice.GetSize())
outImgnp = sitk.GetArrayFromImage(outImg)
lastimgnp = sitk.GetArrayFromImage(nisslslice)
except:
euler2d = [1, 0, 0, 1, 0, 0]
outImg = ndreg2D.imgApplyAffine2D(aavslice,
euler2d,
size=nisslslice.GetSize())
# write out rigid transforms
mytransformfile = open(
outputdirectoryname + patientnumber +
"_AAV_registered_rigidtrans/" + aavnamelist[kk] +
"_rigidtrans.txt", "w")
for item in euler2d:
mytransformfile.write("%s\n" % item)
mytransformfile.close()
kk = kk + 1
# write out image slice
if f < 10:
sitk.WriteImage(
outImg, outputdirectoryname + patientnumber +
'_AAV_registered/000' + str(f) + '.img')
elif f < 100:
sitk.WriteImage(
outImg, outputdirectoryname + patientnumber +
'_AAV_registered/00' + str(f) + '.img')
elif f < 1000:
sitk.WriteImage(
outImg, outputdirectoryname + patientnumber +
'_AAV_registered/0' + str(f) + '.img')
else:
sitk.WriteImage(
outImg, outputdirectoryname + patientnumber +
'_AAV_registered/' + str(f) + '.img')
os.system(
matlabdirectoryname +
" -nodesktop -nojvm -nodisplay -r \"combineRegisteredAAVs_bnb(\'" +
patientnumber + "\');exit\"")
return