def NiiImageInfoStatistic(root_folder, key_word):
case_list = os.listdir(root_folder)
case_list.sort()
shape_list = []
spacing_list = []
for case in case_list:
case_folder = os.path.join(root_folder, case)
candidate_file = glob.glob(os.path.join(case_folder, key_word))
if len(candidate_file) != 1:
print('Not unique file: ', case)
continue
image, _, data = LoadNiiData(candidate_file[0])
shape_list.append(image.GetSize())
spacing_list.append(image.GetSpacing())
shape_info = np.array(shape_list)
spacing_info = np.array(spacing_list)
print('The number of cases is :', len(shape_list))
print('The mean of the size is :', np.mean(shape_info, axis=0))
print('The max of the size is :', np.max(shape_info, axis=0))
print('The min of the size is :', np.min(shape_info, axis=0))
print('The mean of the spacing is :', np.mean(spacing_info, axis=0))
print('The max of the spacing is :', np.max(spacing_info, axis=0))
print('The min of the spacing is :', np.min(spacing_info, axis=0))
import matplotlib.pyplot as plt
fig = plt.figure(0, [15, 5])
ax1 = fig.add_subplot(131)
ax1.hist(spacing_info[:, 0], bins=50)
ax1.set_xlabel('Resolution')
ax1.set_ylabel('Spacing/mm')
ax1.set_title('Histogram of x-axis spacing')
ax2 = fig.add_subplot(132)
ax2.hist(spacing_info[:, 1], bins=50)
ax2.set_xlabel('Resolution')
ax2.set_ylabel('Spacing/mm')
ax2.set_title('Histogram of y-axis spacing')
ax3 = fig.add_subplot(133)
ax3.hist(spacing_info[:, 2], bins=50)
ax3.set_xlabel('Resolution')
ax3.set_ylabel('Spacing/mm')
ax3.set_title('Histogram of z-axis spacing')
plt.show()
if os.path.exists(os.path.join(case_path, 'adc.nii')):
if os.path.exists(os.path.join(case_path, 'dwi.nii')):
bval_path = os.path.join(case_path, 'dwi.bval')
bval = open(bval_path, 'r')
b_list = bval.read().split()
if len(b_list) == 1:
new_img, new_dwi, _ = LoadNiiData(os.path.join(case_path, 'dwi.nii'))
else:
b, index = NearTrueB(b_list)
_, dwi, _ = LoadNiiData(os.path.join(case_path, 'dwi.nii'))
img, _, _ = LoadNiiData(os.path.join(case_path, 'adc.nii'))
new_dwi = dwi[index, ...]
new_img = sitk.GetImageFromArray(new_dwi)
new_img.SetDirection(img.GetDirection())
new_img.SetSpacing(img.GetSpacing())
new_img.SetOrigin(img.GetOrigin())
elif os.path.exists(os.path.join(case_path, 'dki.nii')):
bval_path = os.path.join(case_path, 'dki.bval')
bval = open(bval_path, 'r')
b_list = bval.read().split()
if len(b_list) == 1:
new_img, new_dwi, _ = LoadNiiData(os.path.join(case_path, 'dki.nii'))
else:
b, index = NearTrueB(b_list)
img, dki, _ = LoadNiiData(os.path.join(case_path, 'dki.nii'))
img, _, _ = LoadNiiData(os.path.join(case_path, 'adc.nii'))
new_dwi = dki[index, ...]
new_img = sitk.GetImageFromArray(new_dwi)
new_img.SetDirection(img.GetDirection())
