def MRI_ACR_main(data, results, **kwargs):
acr_test_dict = {
'ACR_GA_l': ACR_GA_l,
'ACR_GA_wh': ACR_GA_wh,
'ACR_GA_whd': ACR_GA_whd,
'ACR_STA': ACR_STA,
'ACR_IIU': ACR_IIU,
'ACR_HCSR': ACR_HCSR,
'ACR_SPA': ACR_SPA,
'ACR_Ghosting': ACR_Ghosting
}
params = kwargs.get('params', None)
print(data.series_filelist)
if len(data.getAllInstances()) > 0:
tmpfile = data.getAllInstances()[0]
for child in params:
print('Performing', child.text)
if child.text in acr_test_dict.keys():
try:
acr_test_dict[child.text](
tmpfile,
tmpfile.pixel_array,
results,
label=tmpfile.SeriesDescription.replace(' ', '_'))
except:
pass
def MRI_ACR_main(data,results, **kwargs):
acr_test_dict = {
'ACR_STA':ACR_STA,
'ACR_IIU':ACR_IIU,
'ACR_HCSR':ACR_HCSR,
'ACR_SPA':ACR_SPA,
'ACR_Ghosting':ACR_Ghosting
}
params = kwargs.get('params', None)
print(data.series_filelist)
if len(data.getAllInstances())>0:
tmpfile = data.getAllInstances()[0]
for child in params:
print child.text
if child.text in acr_test_dict.keys():
try:
acr_test_dict[child.text](tmpfile,tmpfile.pixel_array,results)
except:
pass
def fluoroscopydistortion(data, results, **kwargs):
'''
Function receives an image of a grid and calculates S-distortion and Pincushion distortion.
'''
relevantfile = data.getAllInstances()[0]
#Read DCM file, threshold to separate grid from background
rawdata = relevantfile
pixeldata = rawdata.pixel_array
array_np = np.asarray(pixeldata)
size = array_np.shape[0]
if size != 512:
print 'Resizing image array...'
array_np = scipy.misc.imresize(array_np, (512, 512))
size = array_np.shape[0]
forexport = array_np
mean_pixel = np.mean(pixeldata)
high_values_indices = array_np > mean_pixel
low_values_indices = array_np <= mean_pixel
zero_values_indices = array_np == 0
array_np[high_values_indices] = 2
array_np[low_values_indices] = 1
array_np[zero_values_indices] = 0
resultxml = []
#Define an area for peak detection, resize image matrix if other than 512x512
area = 10
#Plot resized and threholded image
plt.imshow(array_np)
plt.show()
#print (array_np==1).sum()
#Detect intersections by adjacent cell values
dimensionrange = range(size)
xarray = [0]
yarray = [0]
for i in dimensionrange:
for j in dimensionrange:
if array_np[i, j] == 1:
if array_np[i, j - 3] == 1 and array_np[
i,
j - 2] == 1 and array_np[i, j - 1] == 1 and array_np[
i, j +
1] == 1 and array_np[i, j + 2] == 1 and array_np[
i, j + 3] == 1 and array_np[
i - 3, j] == 1 and array_np[
i - 2, j] == 1 and array_np[
i - 1, j] == 1 and array_np[
i + 1, j] == 1 and array_np[
i + 2,
j] == 1 and array_np[
i + 3, j] == 1:
xarray.append(i)
yarray.append(j)
dotsarray = np.zeros((size, size))
dotsarray[xarray, yarray] = 1
#plt.imshow(dotsarray)
#plt.show()
#Detect peaks and plot
coordinates = corner_peaks(dotsarray, min_distance=area)
plt.scatter(coordinates[:, 1], coordinates[:, 0])
plt.show()
#Find center row in grid, sort peaks from left to right, find central peak and determine grid rotation
(subsetx, subsety) = xselector(coordinates, size / 2, 15)
(subsety, subsetx) = [
list(x)
for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))
]
location = findcenter(subsety, size)
rotation = math.degrees(
math.atan(
(float(subsetx[location - 2]) - float(subsetx[location + 2])) /
(float(subsety[location - 2]) - float(subsety[location + 2]))))
print rotation
#Rotate image to correct for grid rotation and plot corrected peaks
rotateddotsarray = scipy.misc.imrotate(dotsarray,
rotation,
interp='bilinear')
rotatedcoordinates = corner_peaks(rotateddotsarray, min_distance=area)
plt.scatter(rotatedcoordinates[:, 1], rotatedcoordinates[:, 0])
plt.show()
#Find center row in rotated grid
(subsetx, subsety) = xselector(rotatedcoordinates, size / 2, 15)
(subsety, subsetx) = [
list(x)
for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))
]
#Determine position of central line to aid in finding consecutive lines in grid
centralline = np.mean(subsetx)
#find central peak of rotated grid
location = findcenter(subsety, size)
ycenter = subsetx[location]
xcenter = subsety[location]
print np.diff(subsety)
#Determine unit size of grid in center of image
#unitsize= np.amin(np.nonzero(np.diff(subsety)))
unitsize = (subsety[location + 1] - subsety[location - 1]) / 2
print unitsize
#Determine the squares x squares area of grid investigated
#squares=6
squares = int(math.floor((0.60 * 512) / unitsize)) / 2
print squares
#Determine distortion in x direction to aid in finding consecutive lines in grid
xdistortion = (float(subsety[location + squares]) - float(
subsety[location - squares])) / (2 * squares * unitsize)
#Determine grid points in lower part of image
(subsetx,
subsety) = xselector(rotatedcoordinates,
centralline - squares * unitsize * xdistortion,
unitsize / 2)
(subsety, subsetx) = [
list(x)
for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))
]
location = findcenter(subsety, size)
#Determine corners of selected square area
x1 = subsety[location - squares]
y1 = subsetx[location - squares]
print(x1, y1)
x3 = subsety[location + squares]
y3 = subsetx[location + squares]
s1 = subsetx[location]
(subsetx,
subsety) = xselector(rotatedcoordinates,
centralline + squares * unitsize * xdistortion,
unitsize / 2)
(subsety, subsetx) = [
list(x)
for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))
]
print subsetx, subsety
location = findcenter(subsety, size)
x2 = subsety[location + squares]
y2 = subsetx[location + squares]
x4 = subsety[location - squares]
y4 = subsetx[location - squares]
s2 = subsetx[location]
pincushion1 = float(
math.sqrt((x2 - x1)**2 + (y2 - y1)**2) /
math.sqrt(2 * (2 * squares * unitsize)**2))
pincushion2 = float(
math.sqrt((x3 - x4)**2 + (y3 - y4)**2) /
math.sqrt(2 * (2 * squares * unitsize)**2))
pincushionratio = float(pincushion2 / pincushion1)
sartifacttop1 = float(s1 - y1)
sartifacttop2 = float(s1 - y3)
sartifactbottom1 = float(s2 - y2)
sartifactbottom2 = float(s2 - y4)
print pincushion1, pincushion2, pincushionratio
results.addFloat('Pincushion 1', pincushion1, level=1)
results.addFloat('Pincushion 2', pincushion2, level=1)
results.addFloat('Pincushion ratio', pincushionratio, level=1)
results.addFloat('S distortion top 1', sartifacttop1, level=1)
results.addFloat('S distortion top 2', sartifacttop2, level=1)
results.addFloat('S distortion bottom 1', sartifactbottom1, level=1)
results.addFloat('S distortion bottom 2', sartifactbottom2, level=1)
#-------------------------
object_naam = 'export.png'
size = pixeldata.shape[0]
if size != 512:
print 'Resizing image array...'
pixeldata = scipy.misc.imresize(pixeldata, (512, 512))
rotated = scipy.misc.imrotate(pixeldata, rotation, interp='bilinear')
rotated[y1 - 3:y1 + 3, x1 - 3:x1 + 3] = 255
rotated[y2 - 3:y2 + 3, x2 - 3:x2 + 3] = 255
rotated[y3 - 3:y3 + 3, x3 - 3:x3 + 3] = 255
rotated[y4 - 3:y4 + 3, x4 - 3:x4 + 3] = 255
rotated[y4 - 3:y4 + 3, x4 - 3:x4 + 3] = 255
rotated[ycenter - 3:ycenter + 3, xcenter - 3:xcenter + 3] = 255
plt.imshow(rotated)
plt.show()
scipy.misc.imsave(object_naam, rotated)
results.addObject('Image', object_naam, level=1)
return
def fluoroscopydistortion(data,results, **kwargs):
'''
Function receives an image of a grid and calculates S-distortion and Pincushion distortion.
'''
relevantfile = data.getAllInstances()[0]
#Read DCM file, threshold to separate grid from background
rawdata=relevantfile
pixeldata=rawdata.pixel_array
array_np = np.asarray(pixeldata)
size=array_np.shape[0]
if size!=512:
print 'Resizing image array...'
array_np=scipy.misc.imresize(array_np,(512,512))
size=array_np.shape[0]
forexport=array_np
mean_pixel=np.mean(pixeldata)
high_values_indices = array_np > mean_pixel
low_values_indices = array_np<=mean_pixel
zero_values_indices = array_np==0
array_np[high_values_indices]=2
array_np[low_values_indices] = 1
array_np[zero_values_indices] = 0
resultxml = []
#Define an area for peak detection, resize image matrix if other than 512x512
area=10
#Plot resized and threholded image
plt.imshow(array_np)
plt.show()
#print (array_np==1).sum()
#Detect intersections by adjacent cell values
dimensionrange=range(size)
xarray=[0]
yarray=[0]
for i in dimensionrange:
for j in dimensionrange:
if array_np[i,j]==1:
if array_np[i,j-3]==1 and array_np[i,j-2]==1 and array_np[i,j-1]==1 and array_np[i,j+1]==1 and array_np[i,j+2]==1 and array_np[i,j+3]==1 and array_np[i-3,j]==1 and array_np[i-2,j]==1 and array_np[i-1,j]==1 and array_np[i+1,j]==1 and array_np[i+2,j]==1 and array_np[i+3,j]==1:
xarray.append(i)
yarray.append(j)
dotsarray=np.zeros((size,size))
dotsarray[xarray,yarray]=1
#plt.imshow(dotsarray)
#plt.show()
#Detect peaks and plot
coordinates=corner_peaks(dotsarray, min_distance=area)
plt.scatter(coordinates[:,1],coordinates[:,0])
plt.show()
#Find center row in grid, sort peaks from left to right, find central peak and determine grid rotation
(subsetx,subsety)=xselector(coordinates,size/2,15)
(subsety,subsetx)=[list(x) for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))]
location=findcenter(subsety,size)
rotation=math.degrees(math.atan((float(subsetx[location-2])-float(subsetx[location+2]))/(float(subsety[location-2])-float(subsety[location+2]))))
print rotation
#Rotate image to correct for grid rotation and plot corrected peaks
rotateddotsarray=scipy.misc.imrotate(dotsarray, rotation, interp='bilinear')
rotatedcoordinates=corner_peaks(rotateddotsarray, min_distance=area)
plt.scatter(rotatedcoordinates[:,1],rotatedcoordinates[:,0])
plt.show()
#Find center row in rotated grid
(subsetx,subsety)=xselector(rotatedcoordinates,size/2,15)
(subsety,subsetx)=[list(x) for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))]
#Determine position of central line to aid in finding consecutive lines in grid
centralline=np.mean(subsetx)
#find central peak of rotated grid
location=findcenter(subsety,size)
ycenter=subsetx[location]
xcenter=subsety[location]
print np.diff(subsety)
#Determine unit size of grid in center of image
#unitsize= np.amin(np.nonzero(np.diff(subsety)))
unitsize= (subsety[location+1]-subsety[location-1])/2
print unitsize
#Determine the squares x squares area of grid investigated
#squares=6
squares=int(math.floor((0.60*512)/unitsize))/2
print squares
#Determine distortion in x direction to aid in finding consecutive lines in grid
xdistortion=(float(subsety[location+squares])-float(subsety[location-squares]))/(2*squares*unitsize)
#Determine grid points in lower part of image
(subsetx,subsety)=xselector(rotatedcoordinates,centralline-squares*unitsize*xdistortion,unitsize/2)
(subsety,subsetx)=[list(x) for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))]
location=findcenter(subsety,size)
#Determine corners of selected square area
x1=subsety[location-squares]
y1=subsetx[location-squares]
print (x1,y1)
x3=subsety[location+squares]
y3=subsetx[location+squares]
s1=subsetx[location]
(subsetx,subsety)=xselector(rotatedcoordinates,centralline+squares*unitsize*xdistortion,unitsize/2)
(subsety,subsetx)=[list(x) for x in zip(*sorted(zip(subsety, subsetx), key=lambda pair: pair[0]))]
print subsetx,subsety
location=findcenter(subsety,size)
x2=subsety[location+squares]
y2=subsetx[location+squares]
x4=subsety[location-squares]
y4=subsetx[location-squares]
s2=subsetx[location]
pincushion1=float(math.sqrt((x2-x1)**2+(y2-y1)**2)/math.sqrt(2*(2*squares*unitsize)**2))
pincushion2=float(math.sqrt((x3-x4)**2+(y3-y4)**2)/math.sqrt(2*(2*squares*unitsize)**2))
pincushionratio=float(pincushion2/pincushion1)
sartifacttop1=float(s1-y1)
sartifacttop2=float(s1-y3)
sartifactbottom1=float(s2-y2)
sartifactbottom2=float(s2-y4)
print pincushion1,pincushion2,pincushionratio
results.addFloat('Pincushion 1', pincushion1,level=1)
results.addFloat('Pincushion 2', pincushion2,level=1)
results.addFloat('Pincushion ratio',pincushionratio,level=1)
results.addFloat('S distortion top 1',sartifacttop1,level=1)
results.addFloat('S distortion top 2',sartifacttop2,level=1)
results.addFloat('S distortion bottom 1',sartifactbottom1,level=1)
results.addFloat('S distortion bottom 2',sartifactbottom2,level=1)
#-------------------------
object_naam = 'export.png'
size=pixeldata.shape[0]
if size!=512:
print 'Resizing image array...'
pixeldata=scipy.misc.imresize(pixeldata,(512,512))
rotated=scipy.misc.imrotate(pixeldata, rotation, interp='bilinear')
rotated[y1-3:y1+3,x1-3:x1+3]=255
rotated[y2-3:y2+3,x2-3:x2+3]=255
rotated[y3-3:y3+3,x3-3:x3+3]=255
rotated[y4-3:y4+3,x4-3:x4+3]=255
rotated[y4-3:y4+3,x4-3:x4+3]=255
rotated[ycenter-3:ycenter+3,xcenter-3:xcenter+3]=255
plt.imshow(rotated)
plt.show()
scipy.misc.imsave(object_naam,rotated)
results.addObject('Image',object_naam,level=1)
return
