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USCurvilinearScanconvert_PWSpectra.py
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USCurvilinearScanconvert_PWSpectra.py
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import itk
import sys
import numpy as np
import matplotlib.pyplot as plt
import skimage.io
import os
import shutil
def GetFileDirNameExt(file_Path):
file_dir, splt_t = os.path.split(file_Path)
file_name, file_ext = os.path.splitext(splt_t)
return file_dir, file_name, file_ext
# def ITKUSCurvilinearScanconvert(itkImg, scan_angle = np.pi/2.0,\
# radius_start = 12.4,\
# radius_stop = 117.5,\
# sz_output = (1108,725),\
# spacing_output = (0.15, 0.15, 0.15)):
# ## Retrive the dimension of inputImage
# dim = itkImg.ImageDimension
# frames = 0
# sz_output = (1108,725)
# origin_output = [0.0, 0.0]
# if dim == 3:
# frames = int(itk.size(itkImg)[2])
# sz_output = (1108,725, frames)
# origin_output = [0.0, 0.0, 0.0]
# ## Rescale the intensity of Image and cast the type of data from float to char
# # iType_USRFFrame = itk.Image[itk.F, dim]
# # iType_CurvilinearImg = itk.CurvilinearArraySpecialCoordinatesImage[itk.UC, dim]
# # iFilter_RescaleIntensity = itk.RescaleIntensityImageFilter[iType_USRFFrame,iType_CurvilinearImg].New()
# # iFilter_RescaleIntensity.SetInput(itkImg)
# # iFilter_RescaleIntensity.UpdateLargestPossibleRegion()
# iType_CurvilinearImg = itk.CurvilinearArraySpecialCoordinatesImage[itk.F, dim]
# iType_SCUSImg = itk.Image[itk.F, dim]
# iFilterCastImage = itk.CastImageFilter[iType_SCUSImg, iType_CurvilinearImg].New()
# ## Put the parameters for curvilinear scan-conversion
# #iimg_curvilinear = itkImg #iFilter_RescaleIntensity.GetOutput()
# iimg_curvilinear = iType_CurvilinearImg.New()
# help(iimg_curvilinear)
# iimg_curvilinear.SetInput(itkImg)
# exit()
# isz_curvilinear = iimg_curvilinear.GetLargestPossibleRegion().GetSize()
# ## Computing the parameters of curvilinear sacn-conversion
# lateral_angular_separation = scan_angle / (isz_curvilinear[1]-1)
# radius_scan = (radius_stop - radius_start) / (isz_curvilinear[0]-1)
# sz_output = (1108,725, frames)
# origin_output = [0.0, 0.0, 0.0]
# origin_output[0] = sz_output[0] * spacing_output[0]/ -2.0
# origin_output[1] = radius_start * np.cos(scan_angle/2)
# iimg_curvilinear.SetLateralAngularSeparation(lateral_angular_separation)
# iimg_curvilinear.SetFirstSampleDistance(radius_start)
# iimg_curvilinear.SetRadiusSampleSize(radius_scan)
# ## Resample Image
# #iType_UCharImg = itk.Image[itk.UC, dim]
# iFilter_resample = itk.ResampleImageFilter[iType_CurvilinearImg, iType_SCUSImg].New()
# iFilter_resample.SetInput(iimg_curvilinear)
# iFilter_resample.SetSize(sz_output)
# iFilter_resample.SetOutputSpacing(spacing_output)
# iFilter_resample.SetOutputOrigin(origin_output)
# iFilter_resample.UpdateLargestPossibleRegion()
# ## Return a scan-converted US BMode image
# return iFilter_resample.GetOutput()
def ITKUSCurvilinearScanconvert_File(input_filepath,\
dim =3,\
scan_angle = np.pi/2.0,\
radius_start = 12.4,\
radius_stop = 117.5,\
sz_output = (1108,725),\
spacing_output = (0.15, 0.15, 0.15)):
## Retrive the dimension of inputImage
#dim = itkImg.ImageDimension
frames = 0
sz_output = (1108,725)
origin_output = [0.0, 0.0]
iType_CurvilinearImg = itk.CurvilinearArraySpecialCoordinatesImage[itk.F, dim]
iType_SCUSImg = itk.Image[itk.F, dim]
iFileReaderUSRF = itk.ImageFileReader[iType_CurvilinearImg].New()
iFileReaderUSRF.SetFileName(input_filepath)
iFileReaderUSRF.UpdateOutputInformation()
iimg_curvilinear = iFileReaderUSRF.GetOutput()
if dim == 3:
frames = int(itk.size(iimg_curvilinear)[2])
sz_output = (1108,725, frames)
origin_output = [0.0, 0.0, 0.0]
spacing_output = (0.15, 0.15, 0.15)
else:
sz_output = (1108,725)
origin_output = [0.0, 0.0]
spacing_output = (0.15, 0.15)
isz_curvilinear = iimg_curvilinear.GetLargestPossibleRegion().GetSize()
## Computing the parameters of curvilinear sacn-conversion
lateral_angular_separation = scan_angle / (isz_curvilinear[1]-1)
radius_scan = (radius_stop - radius_start) / (isz_curvilinear[0]-1)
# sz_output = (1108,725, frames)
# origin_output = [0.0, 0.0, 0.0]
origin_output[0] = sz_output[0] * spacing_output[0]/ -2.0
origin_output[1] = radius_start * np.cos(scan_angle/2)
iimg_curvilinear.SetLateralAngularSeparation(lateral_angular_separation)
iimg_curvilinear.SetFirstSampleDistance(radius_start)
iimg_curvilinear.SetRadiusSampleSize(radius_scan)
## Resample Image
#iType_UCharImg = itk.Image[itk.UC, dim]
iFilter_resample = itk.ResampleImageFilter[iType_CurvilinearImg, iType_SCUSImg].New()
iFilter_resample.SetInput(iimg_curvilinear)
iFilter_resample.SetSize(sz_output)
iFilter_resample.SetOutputSpacing(spacing_output)
iFilter_resample.SetOutputOrigin(origin_output)
iFilter_resample.UpdateLargestPossibleRegion()
## Return a scan-converted US BMode image
return iFilter_resample.GetOutput()
def ScanConvert_ComputedFiles(input_filepath, PWCoef_method):
print ">>>>>>>>>>>>> ScanConvert_ComputedFiles"
print input_filepath
print "<<<<<<<<<<<<< ScanConvert_ComputedFiles"
###
T_file_dir, T_file_name, T_file_ext = GetFileDirNameExt(input_filepath)
T_PWSpectSC_Dir = T_file_dir + '/Scanconverted/'
if not os.path.exists(T_PWSpectSC_Dir):
os.makedirs(T_PWSpectSC_Dir) ## Create a new Folder
else:
shutil.rmtree(T_PWSpectSC_Dir) ## Delete an existing Folder
os.makedirs(T_PWSpectSC_Dir) ## Create a new Folder
itype = itk.Image[itk.F, 2]
iFileWriterPWSpCSC = itk.ImageFileWriter[itype].New()
#T_PWSpectSC_Dir = T_PWSpectSC_Dir + '/'
## Laod a targeted image with input_filepath
dim = 2
if PWCoef_method != 1:
dim = 3
iimage_PWSPectraCoefSC = ITKUSCurvilinearScanconvert_File(input_filepath, dim)
## Save the
## Save the Scanconverted Data as mutiple 2D mha files
iType_SCUS2DImg = itk.Image[itk.F, 2]
iType_SCUS3DImg = itk.Image[itk.F, 3]
print '+++++++++Saving'
print 'dim', dim
if dim == 2:
filepath_output = T_PWSpectSC_Dir+T_file_name+'_SC.mha'
iFileWriterPWSpCSC.SetFileName(filepath_output)
iFileWriterPWSpCSC.SetInput(iimage_PWSPectraCoefSC)
iFileWriterPWSpCSC.Update()
else:
region_SCImg = iimage_PWSPectraCoefSC.GetLargestPossibleRegion()
sz_SCImg = region_SCImg.GetSize()
frames = sz_SCImg[2]
iExtractor_2DSCImg = itk.ExtractImageFilter[iType_SCUS3DImg, iType_SCUS2DImg].New()
iExtractor_2DSCImg.SetInput(iimage_PWSPectraCoefSC)
iExtractor_2DSCImg.SetDirectionCollapseToIdentity()
sz_Extract = (int(sz_SCImg[0]), int(sz_SCImg[1]), 0)
region_Extract = itk.ImageRegion[3]()
region_Extract.SetSize(sz_Extract)
idx_Extract = region_Extract.GetIndex()
for frame in np.arange(frames):
idx_Extract[2] = frame
region_Extract.SetIndex(idx_Extract)
iExtractor_2DSCImg.SetExtractionRegion(region_Extract)
iExtractor_2DSCImg.UpdateLargestPossibleRegion()
filepath_output = T_PWSpectSC_Dir+T_file_name+'_SC_Coef_%d.mha'%frame
iFileWriterPWSpCSC.SetFileName(filepath_output)
iFileWriterPWSpCSC.SetInput(iExtractor_2DSCImg.GetOutput())
iFileWriterPWSpCSC.Update()
## Generate file path
def Pre_ScanConvert_ComputedFiles(rf_filepath, PWCoef_method =1):
## Retrive the method for computing Power Spectrum Coefficient
map_PWCoef_method = { 1:'Integrated', 2:'Chebyshev',
3:'Legendre', 4:'LineRegress' }
method_PWCoef = map_PWCoef_method[PWCoef_method]
T_file_dir, T_file_name, T_file_ext = GetFileDirNameExt(root_target_rf_file)
T_PWSpect_Dir = T_file_dir + '/RF_PWSpectra/'
N1DFFT_R = [32,64] #[32,64,128]
SideLine_R = [2]#[1,2,4,8,16,32]
method = method_PWCoef
if PWCoef_method == 4:
method = 'Line'
print method
for nsfft in N1DFFT_R:
for szline in SideLine_R:
Target_Dir = T_PWSpect_Dir + 'SLine_%03d' %szline +'_NFFT_%03d' %nsfft +'/' + method_PWCoef +'/'
Target_File_F = T_file_name +'_SLine_%03d' %szline +'_NFFT_%03d' %nsfft + '_frame_00009_' + method + '.mha'
Target_FilePath_F = Target_Dir+Target_File_F
print Target_FilePath_F
ScanConvert_ComputedFiles(Target_FilePath_F, PWCoef_method)
# Target_File_A = T_file_name +'_SLine_%03d' %szline +'_NFFT_%03d' %nsfft + '_Avg_' + method + '.mha'
# Target_FilePath_A = Target_Dir+Target_File_A
# print Target_FilePath_A
# ScanConvert_ComputedFiles(Target_FilePath_A, PWCoef_method)
if __name__ == "__main__":
if len(sys.argv) == 3:
print 'input 3'
root_target_rf_file = sys.argv[1]
pwcoefmethod = int(sys.argv[2])
Pre_ScanConvert_ComputedFiles(root_target_rf_file, pwcoefmethod)