def crop_nifti(input_img, ref_crop):
"""Crop input image based on the reference.
It uses nilearn `resample_to_img` function.
Args:
input_img (str): image to be processed
ref_crop (str): template used to crop the image
Returns:
output_img (NIfTI image): crop image on disk.
crop_template: (NIfTI image): output template on disk.
"""
import os
from nilearn.image import crop_img, resample_to_img
basedir = os.getcwd()
# crop_ref = crop_img(ref_img, rtol=0.5)
# crop_ref.to_filename(os.path.join(basedir, os.path.basename(input_img).split('.nii')[0] + '_cropped_template.nii.gz'))
# crop_template = os.path.join(basedir, os.path.basename(input_img).split('.nii')[0] + '_cropped_template.nii.gz')
# resample the individual MRI into the cropped template image
crop_img = resample_to_img(input_img, ref_crop, force_resample=True)
crop_img.to_filename(
os.path.join(
basedir,
os.path.basename(input_img).split(".nii")[0] + "_cropped.nii.gz"))
output_img = os.path.join(
basedir,
os.path.basename(input_img).split(".nii")[0] + "_cropped.nii.gz")
crop_template = ref_crop
return output_img, crop_template
def crop_nifti(input_img, ref_crop):
"""Crop input image based on the reference. It uses nilearn
`resample_to_img` function.
Args:
input_img (str): image to be processed
ref_img (str): template used to crop the image
Returns:
output_img (nifty image): crop image on disk.
crop_template: (nifty image): output template on disk.
"""
import nibabel as nib
import os
import numpy as np
from nilearn.image import resample_img, crop_img, resample_to_img
from nibabel.spatialimages import SpatialImage
basedir = os.getcwd()
# crop_ref = crop_img(ref_img, rtol=0.5)
# crop_ref.to_filename(os.path.join(basedir, os.path.basename(input_img).split('.nii')[0] + '_cropped_template.nii.gz'))
# crop_template = os.path.join(basedir, os.path.basename(input_img).split('.nii')[0] + '_cropped_template.nii.gz')
# resample the individual MRI into the cropped template image
crop_img = resample_to_img(input_img, ref_crop)
crop_img.to_filename(
os.path.join(
basedir,
os.path.basename(input_img).split('.nii')[0] + '_cropped.nii.gz'))
output_img = os.path.join(
basedir,
os.path.basename(input_img).split('.nii')[0] + '_cropped.nii.gz')
crop_template = ref_crop
return output_img, crop_template
def crop_nifti(input_img, ref_img):
"""
:param input_img:
:param crop_sagittal:
:param crop_coronal:
:param crop_axial:
:return:
"""
import nibabel as nib
import os
import numpy as np
from nilearn.image import resample_img, crop_img, resample_to_img
from nibabel.spatialimages import SpatialImage
basedir = os.getcwd()
crop_ref = crop_img(ref_img, rtol=0.5)
crop_ref.to_filename(
os.path.join(
basedir,
os.path.basename(input_img).split('.nii')[0] +
'_cropped_template.nii.gz'))
crop_template = os.path.join(
basedir,
os.path.basename(input_img).split('.nii')[0] +
'_cropped_template.nii.gz')
## resample the individual MRI onto the cropped template image
crop_img = resample_to_img(input_img, crop_template)
crop_img.to_filename(
os.path.join(
basedir,
os.path.basename(input_img).split('.nii')[0] + '_cropped.nii.gz'))
output_img = os.path.join(
basedir,
os.path.basename(input_img).split('.nii')[0] + '_cropped.nii.gz')
return output_img, crop_template