def LabelStatistics(inputLabel, inputVolume, outputCSVFilename):
labelValue = 1
import SimpleITK as sitk
statCalculator = sitk.LabelStatisticsImageFilter()
inImg = sitk.Cast(sitk.ReadImage(inputVolume), sitk.sitkFloat32)
inMsk = sitk.ReadImage(inputLabel)
statCalculator.Execute(inImg, inMsk)
## volume
imageSpacing = inMsk.GetSpacing()
Volume = imageSpacing[0] * imageSpacing[1] * imageSpacing[
2] * statCalculator.GetCount(labelValue)
Mean = statCalculator.GetMean(labelValue)
Median = statCalculator.GetMedian(labelValue)
Minimum = statCalculator.GetMinimum(labelValue)
Maximum = statCalculator.GetMaximum(labelValue)
Sigma = statCalculator.GetSigma(labelValue)
Variance = statCalculator.GetVariance(labelValue)
outputDictionary = dict()
outputDictionary['Mean'] = Mean
outputDictionary['Median'] = Median
outputDictionary['Minimum'] = Minimum
outputDictionary['Maximum'] = Maximum
outputDictionary['Sigma'] = Sigma
outputDictionary['Variance'] = Variance
print "################################################## Mean:: " + str(
Mean)
print "################################################## Median:: " + str(
Median)
print "################################################## Minimum:: " + str(
Minimum)
print "################################################## Maximum:: " + str(
Maximum)
print "################################################## Sigma:: " + str(
Sigma)
print "################################################## Variance:: " + str(
Variance)
## TODO 25/75 quantiles
LowerHalfMsk = sitk.BinaryThreshold(inImg, Minimum, Median)
Quantile25Calculator = sitk.LabelStatisticsImageFilter()
Quantile25Calculator.Execute(inImg, inMsk * LowerHalfMsk)
Quantile25 = Quantile25Calculator.GetMedian(labelValue)
outputDictionary['Quantile25'] = Quantile25
print "################################################## Quantile25:: " + str(
Quantile25)
UpperHalfMsk = sitk.BinaryThreshold(inImg, Median, Maximum)
Quantile75Calculator = sitk.LabelStatisticsImageFilter()
Quantile75Calculator.Execute(inImg, inMsk * UpperHalfMsk)
Quantile75 = Quantile75Calculator.GetMedian(labelValue)
outputDictionary['Quantile75'] = Quantile75
print "################################################## Quantile75:: " + str(
Quantile75)
## TODO MAD
AbsoluteFilter = sitk.AbsImageFilter()
AbsImg = AbsoluteFilter.Execute(inImg - Median)
MADCalculator = sitk.LabelStatisticsImageFilter()
MADCalculator.Execute(AbsImg, inMsk)
MAD = MADCalculator.GetMedian(labelValue)
outputDictionary['MAD'] = MAD
print "################################################## MAD:: " + str(
MAD)
import csv
csvFile = open(outputCSVFilename, 'w')
dWriter = csv.DictWriter(csvFile, outputDictionary.keys())
dWriter.writeheader()
dWriter.writerow(outputDictionary)
import os
import sys
returnFile = os.path.realpath(outputCSVFilename)
outputDictionarySet = dict(volume=inputVolume,
labelVolume=inputLabel,
statDict=outputDictionary)
# returnDict = { 'outputStatDictionary': outputDictionary,
# 'outputCSVFilename': returnFile }
# return returnDict
return returnFile, outputDictionarySet
def LabelStatistics(inputLabel, inputVolume, outputCSVFilename):
labelValue = 1
import SimpleITK as sitk
from collections import (
OrderedDict,
) # Need OrderedDict internally to ensure consistent ordering
statCalculator = sitk.LabelStatisticsImageFilter()
inImg = sitk.Cast(sitk.ReadImage(inputVolume), sitk.sitkFloat32)
inMsk = sitk.ReadImage(inputLabel)
statCalculator.Execute(inImg, inMsk)
## volume
imageSpacing = inMsk.GetSpacing()
Volume = (
imageSpacing[0]
* imageSpacing[1]
* imageSpacing[2]
* statCalculator.GetCount(labelValue)
)
Mean = statCalculator.GetMean(labelValue)
Median = statCalculator.GetMedian(labelValue)
Minimum = statCalculator.GetMinimum(labelValue)
Maximum = statCalculator.GetMaximum(labelValue)
Sigma = statCalculator.GetSigma(labelValue)
Variance = statCalculator.GetVariance(labelValue)
outputDictionary = OrderedDict()
outputDictionary["Mean"] = Mean
outputDictionary["Median"] = Median
outputDictionary["Minimum"] = Minimum
outputDictionary["Maximum"] = Maximum
outputDictionary["Sigma"] = Sigma
outputDictionary["Variance"] = Variance
print(("################################################## Mean:: " + str(Mean)))
print(
("################################################## Median:: " + str(Median))
)
print(
("################################################## Minimum:: " + str(Minimum))
)
print(
("################################################## Maximum:: " + str(Maximum))
)
print(("################################################## Sigma:: " + str(Sigma)))
print(
(
"################################################## Variance:: "
+ str(Variance)
)
)
## TODO 25/75 quantiles
LowerHalfMsk = sitk.BinaryThreshold(inImg, Minimum, Median)
Quantile25Calculator = sitk.LabelStatisticsImageFilter()
Quantile25Calculator.Execute(inImg, inMsk * LowerHalfMsk)
Quantile25 = Quantile25Calculator.GetMedian(labelValue)
outputDictionary["Quantile25"] = Quantile25
print(
(
"################################################## Quantile25:: "
+ str(Quantile25)
)
)
UpperHalfMsk = sitk.BinaryThreshold(inImg, Median, Maximum)
Quantile75Calculator = sitk.LabelStatisticsImageFilter()
Quantile75Calculator.Execute(inImg, inMsk * UpperHalfMsk)
Quantile75 = Quantile75Calculator.GetMedian(labelValue)
outputDictionary["Quantile75"] = Quantile75
print(
(
"################################################## Quantile75:: "
+ str(Quantile75)
)
)
## TODO MAD
AbsoluteFilter = sitk.AbsImageFilter()
AbsImg = AbsoluteFilter.Execute(inImg - Median)
MADCalculator = sitk.LabelStatisticsImageFilter()
MADCalculator.Execute(AbsImg, inMsk)
MAD = MADCalculator.GetMedian(labelValue)
outputDictionary["MAD"] = MAD
print(("################################################## MAD:: " + str(MAD)))
import csv
csvFile = open(outputCSVFilename, "w")
dWriter = csv.DictWriter(csvFile, list(outputDictionary.keys()))
dWriter.writeheader()
dWriter.writerow(outputDictionary)
import os
import sys
from collections import (
OrderedDict,
) # Need OrderedDict internally to ensure consistent ordering
returnFile = os.path.realpath(outputCSVFilename)
outputDictionarySet = OrderedDict(
volume=inputVolume, labelVolume=inputLabel, statDict=outputDictionary
)
# returnDict = { 'outputStatDictionary': outputDictionary,
# 'outputCSVFilename': returnFile }
# return returnDict
return returnFile, outputDictionarySet
