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| import os | |
| import nibabel as nib | |
| import SimpleITK as sitk | |
| from nibabel import processing | |
| from nibabel.orientations import aff2axcodes, axcodes2ornt, ornt_transform | |
| def N4_Bias_Field_Correction(input_path, output_path): | |
| """ | |
| Perform N4 Bias Field Correction on an input image and save the corrected image to the specified output path. | |
| Args: | |
| input_path (str): Path to the input image file. | |
| output_path (str): Path to save the corrected image file. | |
| Returns: | |
| None | |
| """ | |
| raw_img_sitk = sitk.ReadImage(input_path, sitk.sitkFloat32) | |
| transformed = sitk.RescaleIntensity(raw_img_sitk, 0, 255) | |
| transformed = sitk.LiThreshold(transformed, 0, 1) | |
| head_mask = transformed | |
| shrinkFactor = 4 | |
| inputImage = sitk.Shrink(raw_img_sitk, [shrinkFactor] * raw_img_sitk.GetDimension()) | |
| maskImage = sitk.Shrink(head_mask, [shrinkFactor] * raw_img_sitk.GetDimension()) | |
| bias_corrector = sitk.N4BiasFieldCorrectionImageFilter() | |
| corrected = bias_corrector.Execute(inputImage, maskImage) | |
| log_bias_field = bias_corrector.GetLogBiasFieldAsImage(raw_img_sitk) | |
| corrected_image_full_resolution = raw_img_sitk / sitk.Exp(log_bias_field) | |
| sitk.WriteImage(corrected_image_full_resolution, output_path) | |
| return | |
| def preprocessing(ipath, output_dir, basename): | |
| """ | |
| Preprocesses a medical image by performing N4 bias field correction and conforming the image to a specified shape and voxel size. | |
| Args: | |
| ipath (str): The input file path of the medical image to be processed. | |
| output_dir (str): The directory where the processed image will be saved. | |
| basename (str): The base name for the output file. | |
| Returns: | |
| tuple: A tuple containing: | |
| - odata (nibabel.Nifti1Image): The N4 bias field corrected image. | |
| - data (nibabel.Nifti1Image): The conformed image with specified shape and voxel size. | |
| """ | |
| opath = os.path.join(output_dir, f"original/{basename}_N4.nii") | |
| N4_Bias_Field_Correction(ipath, opath) | |
| odata = nib.squeeze_image(nib.as_closest_canonical(nib.load(opath))) | |
| data = processing.conform(odata, out_shape=(256, 256, 256), voxel_size=(1.0, 1.0, 1.0), order=1) | |
| return odata, data | |