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utilities.py
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utilities.py
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import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
def hist_match(source, template):
"""
Adjust the pixel values of a grayscale image such that its histogram
matches that of a target image
Arguments:
-----------
source: np.ndarray
Image to transform; the histogram is computed over the flattened
array
template: np.ndarray
Template image; can have different dimensions to source
Returns:
-----------
matched: np.ndarray
The transformed output image
"""
oldshape = source.shape
source = source.ravel()
template = template.ravel()
# get the set of unique pixel values and their corresponding indices and
# counts
s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
return_counts=True)
t_values, t_counts = np.unique(template, return_counts=True)
# take the cumsum of the counts and normalize by the number of pixels to
# get the empirical cumulative distribution functions for the source and
# template images (maps pixel value --> quantile)
s_quantiles = np.cumsum(s_counts).astype(np.float64)
s_quantiles /= s_quantiles[-1]
t_quantiles = np.cumsum(t_counts).astype(np.float64)
t_quantiles /= t_quantiles[-1]
# interpolate linearly to find the pixel values in the template image
# that correspond most closely to the quantiles in the source image
#interp_t_values = np.zeros_like(source,dtype=float)
interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
return interp_t_values[bin_idx].reshape(oldshape)
def sitk_show(nda, title=None, margin=0.0, dpi=40):
figsize = (1 + margin) * nda.shape[0] / dpi, (1 + margin) * nda.shape[1] / dpi
extent = (0, nda.shape[1], nda.shape[0], 0)
fig = plt.figure(figsize=figsize, dpi=dpi)
ax = fig.add_axes([margin, margin, 1 - 2*margin, 1 - 2*margin])
plt.set_cmap("gray")
for k in range(0,nda.shape[2]):
print "printing slice "+str(k)
ax.imshow(np.squeeze(nda[:,:,k]),extent=extent,interpolation=None)
plt.draw()
plt.pause(0.1)
#plt.waitforbuttonpress()
def computeQualityMeasures(lP,lT):
quality=dict()
labelPred=sitk.GetImageFromArray(lP, isVector=False)
labelTrue=sitk.GetImageFromArray(lT, isVector=False)
hausdorffcomputer=sitk.HausdorffDistanceImageFilter()
hausdorffcomputer.Execute(labelTrue>0.5,labelPred>0.5)
quality["avgHausdorff"]=hausdorffcomputer.GetAverageHausdorffDistance()
quality["Hausdorff"]=hausdorffcomputer.GetHausdorffDistance()
dicecomputer=sitk.LabelOverlapMeasuresImageFilter()
dicecomputer.Execute(labelTrue>0.5,labelPred>0.5)
quality["dice"]=dicecomputer.GetDiceCoefficient()
return quality
def produceRandomlyDeformedImage(image, label, numcontrolpoints, stdDef):
sitkImage=sitk.GetImageFromArray(image, isVector=False)
sitklabel=sitk.GetImageFromArray(label, isVector=False)
transfromDomainMeshSize=[numcontrolpoints]*sitkImage.GetDimension()
tx = sitk.BSplineTransformInitializer(sitkImage,transfromDomainMeshSize)
params = tx.GetParameters()
paramsNp=np.asarray(params,dtype=float)
paramsNp = paramsNp + np.random.randn(paramsNp.shape[0])*stdDef
paramsNp[0:int(len(params)/3)]=0 #remove z deformations! The resolution in z is too bad
params=tuple(paramsNp)
tx.SetParameters(params)
resampler = sitk.ResampleImageFilter()
resampler.SetReferenceImage(sitkImage)
resampler.SetInterpolator(sitk.sitkLinear)
resampler.SetDefaultPixelValue(0)
resampler.SetTransform(tx)
resampler.SetDefaultPixelValue(0)
outimgsitk = resampler.Execute(sitkImage)
outlabsitk = resampler.Execute(sitklabel)
outimg = sitk.GetArrayFromImage(outimgsitk)
outimg = outimg.astype(dtype=np.float32)
outlbl = sitk.GetArrayFromImage(outlabsitk)
outlbl = (outlbl>0.5).astype(dtype=np.float32)
return outimg,outlbl
def produceRandomlyTranslatedImage(image, label):
sitkImage = sitk.GetImageFromArray(image, isVector=False)
sitklabel = sitk.GetImageFromArray(label, isVector=False)
itemindex = np.where(label > 0)
randTrans = (0,np.random.randint(-np.min(itemindex[1])/2,(image.shape[1]-np.max(itemindex[1]))/2),np.random.randint(-np.min(itemindex[0])/2,(image.shape[0]-np.max(itemindex[0]))/2))
translation = sitk.TranslationTransform(3, randTrans)
resampler = sitk.ResampleImageFilter()
resampler.SetReferenceImage(sitkImage)
resampler.SetInterpolator(sitk.sitkLinear)
resampler.SetDefaultPixelValue(0)
resampler.SetTransform(translation)
outimgsitk = resampler.Execute(sitkImage)
outlabsitk = resampler.Execute(sitklabel)
outimg = sitk.GetArrayFromImage(outimgsitk)
outimg = outimg.astype(dtype=float)
outlbl = sitk.GetArrayFromImage(outlabsitk) > 0
outlbl = outlbl.astype(dtype=float)
return outimg, outlbl