def Deformation(mask_segm_path, par_path, method, pnr, dir_res_i, TRANSFORMIX_PATH):
dir_res = dir_res_i+"Transformed_masks/mask_{}_{}".format(method, pnr)
if os.path.exists(dir_res_i+"Transformed_masks/") is False:
os.mkdir(dir_res_i+"Transformed_masks/")
if os.path.exists(dir_res_i+"Transformed_masks/mask_{}_{}".format(method, pnr)) is False:
os.mkdir(dir_res_i+"Transformed_masks/mask_{}_{}".format(method, pnr))
path = new_param_file(par_path, "FinalBSplineInterpolationOrder", '3', '0')
# Make a new transformix object tr with the CORRECT PATH to transformix
tr = elastix.TransformixInterface(parameters=path,
transformix_path=TRANSFORMIX_PATH)
# Transform the mask with the transformation parameters
transformed_image_path = tr.transform_image(mask_segm_path, output_dir=dir_res)
transf_mask = sitk.ReadImage(transformed_image_path)
transf_mask = sitk.GetArrayFromImage(transf_mask )
# Get the Jacobian matrix (not yet used)
jacobian_matrix_path = tr.jacobian_matrix(output_dir=dir_res)
# Get the Jacobian determinant (not yet used)
jacobian_determinant_path = tr.jacobian_determinant(output_dir=dir_res)
# Get the full deformation field (not yet used)
deformation_field_path = tr.deformation_field(output_dir=dir_res)
return transf_mask, transformed_image_path
def write_transform_images():
image_paths = get_image_paths(fixed_img_name, moving_img_name)
results_dir = f"{IMAGES_PATH}/{fixed_img_name}-{moving_img_name}"
make_dir(results_dir)
print("register", end="", flush=True)
el.register(fixed_image=image_paths[0],
moving_image=image_paths[1],
parameters=param_array,
output_dir=results_dir,
verbose=False)
print(" | transform ", end="", flush=True)
nr_params = len(param_array)
for i in range(nr_params):
print(".", end="", flush=True)
temp_dir = f"{results_dir}/{i}"
temp_mr_dir = f"{temp_dir}/mr"
temp_pros_dir = f"{temp_dir}/pros"
make_dir(temp_dir)
make_dir(temp_mr_dir)
make_dir(temp_pros_dir)
transform_path = os.path.join(results_dir,
f"TransformParameters.{i}.txt")
transform = elastix.TransformixInterface(
parameters=transform_path, transformix_path=TRANSFORMIX_PATH)
transform_img(image_paths[1], temp_mr_dir, transform)
transform_img(image_paths[3], temp_pros_dir, transform)
print(" | ", end="", flush=True)
def transform2d(movingnr, slicenr, runnr, transformmask=True):
movingnr = str(movingnr) + '_' + str(slicenr)
transform_path = os.path.join(f'results{runnr}',
r'TransformParameters.0.txt')
moving_image_path = os.path.join(data_path, f"p{movingnr}\mr_bffe.mhd")
output_path = f'results{runnr}'
#make a new transformix object tr
tr = elastix.TransformixInterface(parameters=transform_path,
transformix_path=transformix_path)
#transform a new image with the transformation parameters
tr.transform_image(moving_image_path, output_dir=output_path)
#get the Jacobian matrix
tr.jacobian_matrix(output_dir=output_path)
#get the Jacobian determinant
tr.jacobian_determinant(output_dir=output_path)
# Get the full deformation field
tr.deformation_field(output_dir=output_path)
if transformmask:
moving_image_path = os.path.join(data_path,
f"p{movingnr}\prostaat.mhd")
#Change BSplineInterpolationOrder in parameterfile for mask transform
interporder = readparameter(transform_path,
"FinalBSplineInterpolationOrder")
replace(transform_path, "FinalBSplineInterpolationOrder", 0)
#make a new transformix object tr
tr = elastix.TransformixInterface(parameters=transform_path,
transformix_path=transformix_path)
output_path += r'\transformedmask'
if os.path.exists(output_path) is False:
os.mkdir(output_path)
#transform a new image with the transformation parameter
tr.transform_image(moving_image_path, output_dir=output_path)
#Change back BSplineInterpolationOrder in parameterfile
replace(transform_path, "FinalBSplineInterpolationOrder", interporder)
def get_transform(fixed_img_path, moving_img_path):
el.register(fixed_image=fixed_img_path,
moving_image=moving_img_path,
parameters=param_array,
output_dir=TEMP_RESULTS_PATH,
verbose=False)
transform_path = os.path.join(
TEMP_RESULTS_PATH, f"TransformParameters.{len(param_array) - 1}.txt")
return elastix.TransformixInterface(parameters=transform_path,
transformix_path=TRANSFORMIX_PATH)
def registration(parameters, fix_im_p, mov_im_p, mov_im_m_p, name):
ELASTIX_PATH = os.path.join(
r'C:\Users\s148534\PycharmProjects\myfolder\elastix.exe')
TRANSFORMIX_PATH = os.path.join(
r'C:\Users\s148534\PycharmProjects\myfolder\transformix.exe')
if not os.path.exists(ELASTIX_PATH):
raise IOError(
'Elastix cannot be found, please set the correct ELASTIX_PATH.')
if not os.path.exists(TRANSFORMIX_PATH):
raise IOError(
'Transformix cannot be found, please set the correct TRANSFORMIX_PATH.'
)
# Define a new elastix object 'el' with the correct path to elastix
el = elastix.ElastixInterface(elastix_path=ELASTIX_PATH)
# Make a results directory if none exists
if not os.path.exists('results' + name):
os.mkdir('results' + name)
# # Execute the registration. Make sure the paths below are correct, and
# that the results folder exists from where you are running this script
parm = parameters
el.register(fixed_image=fix_im_p,
moving_image=mov_im_p,
parameters=[os.path.join(parm)],
output_dir='results' + name)
# Find the results
transform_path = os.path.join('results' + name,
'TransformParameters.0.txt')
tr_im_p = os.path.join('results' + name, 'result.0.mhd')
# Make a new transformix object tr with the CORRECT PATH to transformix
tr = elastix.TransformixInterface(parameters=transform_path,
transformix_path=TRANSFORMIX_PATH)
# Make a results directory if none exists
if not os.path.exists('results_tr' + name):
os.mkdir('results_tr' + name)
tr_im_m_p = tr.transform_image(mov_im_m_p, output_dir=r'results_tr' + name)
return tr_im_p, tr_im_m_p
def register_get_transform(input_fixed_img_path, input_moving_img_path,
input_param_array):
# Execute the registration.
print("registration", end=" ", flush=True)
begin_time = time.time()
el.register(fixed_image=input_fixed_img_path,
moving_image=input_moving_img_path,
parameters=input_param_array,
output_dir=results_dir)
global duration
duration = time.gmtime(time.time() - begin_time)
# Make a new transformix object
print("transformation", end=" ", flush=True)
transform_path = os.path.join(
results_dir, f"TransformParameters.{len(input_param_array) - 1}.txt")
return_transform = elastix.TransformixInterface(
parameters=transform_path, transformix_path=TRANSFORMIX_PATH)
return return_transform
def Transform_GT(data_path, results_path, transformix_path):
"""Takes as input a file path to:
1. Folder where data is stored
2. Folder where results are stored
3. Path to transformix.exe file
This function applies the registration found for moving -> fixed image to the masks (groundtruth, GT) of the
moving image, then writes this as .mhd & .raw files to the same folder as where the registration result &
parameter file was written. """
mri, masks_paths = Import_Files_string(data_path)
# Store all path names in a dictionary according to their patient ID for easy referencing
# via slicing out path names
masks_dict = {}
for path in masks_paths:
masks_dict[path[-17:-13]] = path
# Collect a list of all transform parameter files
files = glob.glob(results_path + r'\p*\p*\TransformParameters.0.txt')
# Setting "FinalBSplineInterpolationOrder" to 0 prevents circular pixelated edges of transferred masks(doesnt draw a thrid order polynomial.)
for current in files:
with fileinput.FileInput(current, inplace=True) as file:
for line in file:
print(line.replace("(FinalBSplineInterpolationOrder 3)",
"(FinalBSplineInterpolationOrder 0)"),
end='')
# Apply transformation of parameterfile i to its respective mask, then write to file
nFiles = len(files)
for n, parameterfile in zip(range(nFiles), files):
# Print progress
print('{} registrations out of {} performed'.format(n, nFiles))
current_path = parameterfile[:-25]
T = elastix.TransformixInterface(parameters=parameterfile,
transformix_path=transformix_path)
path_to_transformed_image = T.transform_image(
image_path=masks_dict[parameterfile[-30:-26]],
output_dir=current_path)
moving_image=mr_image_path,
parameters=[os.path.join('parameterfiles', 'parameters_affine.txt'), os.path.join('parameterfiles', 'parameters_bspline.txt')],
output_dir=result_path)
final_result_path = os.path.join(result_path, 'result.1.mhd')
transform_path = os.path.join(result_path, 'TransformParameters.1.txt')
transform_path0 = os.path.join(result_path, 'TransformParameters.0.txt')
transform_path1 = os.path.join(result_path, 'TransformParameters.1.txt')
final_transform_path = os.path.join(result_path, 'transform_pathfinal.txt')
# Change FinalBSplineInterpolationOrder to 0 for binary mask transformation
TransformParameterFileEditor(transform_path1, transform_path0, final_transform_path).modify_transform_parameter_file()
# Make a new transformix object tr with the CORRECT PATH to transformix
tr = elastix.TransformixInterface(parameters=final_transform_path,
transformix_path=TRANSFORMIX_PATH)
transformed_pr_path = tr.transform_image(pr_image_path, output_dir=result_path)
image_array_tpr = sitk.GetArrayFromImage(sitk.ReadImage(transformed_pr_path))
log_path = os.path.join(result_path, 'IterationInfo.1.R3.txt')
log = elastix.logfile(log_path)
DSC.append(dice_coef(image_array_opr, image_array_tpr))
SNS.append(sensitivity(image_array_opr, image_array_tpr))
SPC.append(specificity(image_array_opr, image_array_tpr))
finalMI.append(statistics.mean(log['metric'][-50:-1]))
fig, (ax1,ax2,ax3) = plt.subplots(1, 3, figsize=(15, 5))
ax1.scatter(finalMI,DSC)
ax1.set_title("DSC")
def register_patients(fixed_subject, moving_subject, result_dir_start):
image_folder = "TrainingData"
param_file = 'Training/3Dtrans.txt'
param_file_spline = 'Training/3Dspline.txt'
result_dir = os.path.join(result_dir_start, "trans")
fixed_subject_path = os.path.join(image_folder, fixed_subject)
moving_subject_path = os.path.join(image_folder, moving_subject)
fixed_img_path = os.path.join(fixed_subject_path, 'mr_bffe.mhd')
fixed_seg_img_path = os.path.join(fixed_subject_path, 'prostaat.mhd')
moving_img_path = os.path.join(moving_subject_path, 'mr_bffe.mhd')
moving_seg_img_path = os.path.join(moving_subject_path, 'prostaat.mhd')
fixed_img = sitk.GetArrayFromImage(sitk.ReadImage(fixed_img_path))
fixed_seg_img = sitk.GetArrayFromImage(sitk.ReadImage(fixed_seg_img_path))
moving_img = sitk.GetArrayFromImage(sitk.ReadImage(moving_img_path))
moving_seg_img = sitk.GetArrayFromImage(
sitk.ReadImage(moving_seg_img_path))
# Define a new elastix object 'el' with the correct path to elastix
el = elastix.ElastixInterface(elastix_path=ELASTIX_PATH)
# Make a results directory if none exists
if not os.path.exists(result_dir):
os.mkdir(result_dir)
elif len(os.listdir(result_dir)) != 0:
inp = input("OK to overwrite? (y/n) ")
if inp != 'y':
sys.exit()
# Execute the registration. Make sure the paths below are correct, and
# that the results folder exists from where you are running this script
##pre-translation of the moving image
el.register(fixed_image=fixed_img_path,
moving_image=moving_img_path,
parameters=[param_file],
output_dir=result_dir)
# Find the results
transform_path = os.path.join(result_dir, 'TransformParameters.0.txt')
#result_path = os.path.join(result_dir, 'result.0.mhd')
'''
# Open the logfile into the dictionary log
for i in range(5):
log_path = os.path.join(result_dir, 'IterationInfo.0.R{}.txt'.format(i))
log = elastix.logfile(log_path)
# Plot the 'metric' against the iteration number 'itnr'
plt.plot(log['itnr'], log['metric'])
plt.legend(['Resolution {}'.format(i) for i in range(5)])
'''
#transformed_moving_image = sitk.GetArrayFromImage(sitk.ReadImage(os.path.join(result_dir,'result.0.mhd')))
# Make a new transformix object tr with the CORRECT PATH to transformix
tr = elastix.TransformixInterface(parameters=transform_path,
transformix_path=TRANSFORMIX_PATH)
# Transform a new image with the transformation parameters
img_out = os.path.join(result_dir, 'image')
if not os.path.exists(img_out):
os.mkdir(img_out)
seg_out = os.path.join(result_dir, 'segmentation')
if not os.path.exists(seg_out):
os.mkdir(seg_out)
t_img_path = tr.transform_image(moving_img_path, output_dir=img_out)
t_seg_path = tr.transform_image(moving_seg_img_path, output_dir=seg_out)
#t_img = sitk.GetArrayFromImage(sitk.ReadImage(t_img_path))
#t_seg_img = sitk.GetArrayFromImage(sitk.ReadImage(t_seg_path))
##B spline registration
result_dir = os.path.join(result_dir_start, 'Bspline')
if not os.path.exists(result_dir):
os.mkdir(result_dir)
el.register(fixed_image=fixed_img_path,
moving_image=t_img_path,
parameters=[param_file_spline],
output_dir=result_dir)
transform_path2 = os.path.join(result_dir, 'TransformParameters.0.txt')
#result_path2 = os.path.join(result_dir, 'result.0.mhd')
tr = elastix.TransformixInterface(parameters=transform_path2,
transformix_path=TRANSFORMIX_PATH)
# Transform a new image with the transformation parameters
img_out = os.path.join(result_dir, 'image')
if not os.path.exists(img_out):
os.mkdir(img_out)
seg_out = os.path.join(result_dir, 'segmentation')
if not os.path.exists(seg_out):
os.mkdir(seg_out)
t_img_path2 = tr.transform_image(t_img_path, output_dir=img_out)
t_seg_path2 = tr.transform_image(t_seg_path, output_dir=seg_out)
t_img2 = sitk.GetArrayFromImage(sitk.ReadImage(t_img_path2))
t_seg_img2 = sitk.GetArrayFromImage(sitk.ReadImage(t_seg_path2))
dice = dice_overlap(t_seg_img2, fixed_seg_img)
print('Dice similarity score with ground truth is {}'.format(dice))
#visualization of the results
visualize_patient(fixed_img, fixed_seg_img, moving_img, moving_seg_img,
t_img2, t_seg_img2)
'''
for i in range(np.shape(t_seg_img2)[0]):
fig, ax = plt.subplots(1, 3, figsize=(10, 15))
ax[0].imshow(moving_seg_img[i,:,:], cmap='gray')
ax[0].set_title('Ground Truth')
ax[1].imshow(t_seg_img2[i,:,:], cmap='gray')
ax[1].set_title('Estimation')
ax[2].imshow(fixed_seg_img[i,:,:], cmap='gray')
ax[2].set_title('Fixed Atlas')
plt.show()
#make video of images
out = cv2.VideoWriter('project.avi',cv2.VideoWriter_fourcc('M','J','P','G'), float(10), (np.shape(t_img2)[2],np.shape(t_img2)[1]), isColor=True)
for i in range(np.shape(t_seg_img2)[0]):
out.write(t_seg_img2[i,:,:])
out.release()
'''
#
## Get the Jacobian matrix
#jacobian_matrix_path = tr.jacobian_matrix(output_dir=result_dir)
#
## Get the Jacobian determinant
#jacobian_determinant_path = tr.jacobian_determinant(output_dir=result_dir)
#
## Get the full deformation field
#deformation_field_path = tr.deformation_field(output_dir=result_dir)
#
#jacobian_image = imageio.imread(jacobian_determinant_path.replace('dcm', 'tiff'))
#jacobian_binary = jacobian_image>0
#
## Add a plot of the Jacobian determinant (in this case, the file is a tiff file)
#ax[3].imshow(jacobian_binary,cmap='gray')
#ax[3].set_title('Jacobian\ndeterminant')
return t_seg_img2, dice
image_array_s = sitk.GetArrayFromImage(itk_image)
f_image = sitk.ReadImage(fixed_image_path)[:,:,20]
fixed_image_s = sitk.GetArrayFromImage(f_image)
m_image = sitk.ReadImage(moving_image_path)[:,:,20]
moving_image_s = sitk.GetArrayFromImage(m_image)
f, (ax0, ax1, ax2,ax3) = plt.subplots(1, 4)
ax0.imshow(fixed_image_s, cmap='gray')
ax0.set_title('fixed')
ax1.imshow(moving_image_s, cmap='gray')
ax1.set_title('moving')
ax2.imshow(image_array_s, cmap='gray')
ax2.set_title('registered image')
# Make a new transformix object tr with the CORRECT PATH to transformix
tr = elastix.TransformixInterface(parameters=os.path.join(dir_res, 'TransformParameters.0.txt'),
transformix_path=TRANSFORMIX_PATH)
# Transform a new image with the transformation parameters
transformed_image_path = tr.transform_image(moving_image_path, output_dir=dir_res)
# Get the Jacobian matrix
jacobian_matrix_path = tr.jacobian_matrix(output_dir=dir_res)
# Get the Jacobian determinant
jacobian_determinant_path = tr.jacobian_determinant(output_dir=dir_res)
# Get the full deformation field
deformation_field_path = tr.deformation_field(output_dir=dir_res)
ax3.imshow(imageio.imread(jacobian_determinant_path.replace('dcm', 'tiff'))[40])
