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import glob
import os
from CTevaluate import *
def singleevaluate(file_path, window_width = 150, window_level = 30):
# Load the NIfTI CT image data using nibabel.
if file_path.endswith('.nii.gz'):
ct_image_nifti = nib.load(file_path)
ct_image_data = ct_image_nifti.get_fdata()
ct_image_nifti = nib.load(file_path)
ct_image_data = ct_image_nifti.get_fdata()
elif file_path.endswith('.nrrd'):
ct_image_data, header = nrrd.read(file_path)
ct_data_shape=ct_image_data.shape
#plot_ct_value_distribution(ct_image_data)
ct_image_data = ct_windowing(ct_image_data, window_width, window_level)
#plot_ct_value_distribution(ct_image_data)
# cut roi
center_x = ct_image_data.shape[0] // 2
center_y = ct_image_data.shape[1] // 2
ct_image_roi=extract_roi(ct_image_data, center_x=center_x, center_y=center_y, length=300, width=300)
ct_image_roi_mean=np.mean(ct_image_roi)
# Calculate contrast and standard deviation of CT values.
contrast = calculate_contrast(ct_image_data)
std_deviation = calculate_standard_deviation(ct_image_data)
return ct_image_roi_mean, contrast, std_deviation, ct_data_shape
def batchevaluate(dataset_path, format='.nii.gz', save_path='', nii_name='test'):
for patient_data in glob.glob(dataset_path + "/*"):
if patient_data.endswith(format):
patient_name=os.path.basename(os.path.normpath(patient_data))
print('-------------', patient_name, '-------------')
ct_image_roi_mean, contrast, std_deviation, ct_data_shape = singleevaluate(patient_data)
with open(os.path.join(save_path, f'{nii_name}.txt'), 'a') as f:
f.write('-------------'+patient_name+'-------------\n')
f.write('Mean of CT values in ROI: '+str(ct_image_roi_mean)+'\n')
f.write('Contrast of CT image: '+str(contrast)+'\n')
f.write('Standard Deviation of CT values: '+str(std_deviation)+'\n')
f.write('Size of CT image: '+str(ct_data_shape)+'\n')
def main():
dataset_path=r'D:\Data\dataNeaotomAlpha\NIFTI23072115'
batchevaluate(dataset_path=dataset_path, format='.nii.gz', save_path=dataset_path, nii_name='evaluate')
if __name__=="__main__":
main() |