Convert NIfTI images into multiple TIF images

nii_to_tif.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
nii_to_tif

command line executable to convert 3d nifti images to 
individual tiff images along a user-specified axis

call as: python nii_to_tif.py /path/to/nifti /path/to/tif
(append optional arguments to the call as desired)

Author: Jacob Reinhold ([email protected])
"""

import argparse
from glob import glob
import os
import sys

from PIL import Image
import nibabel as nib
import numpy as np


def arg_parser():
    parser = argparse.ArgumentParser(description='split 3d image into multiple 2d images')
    parser.add_argument('img_dir', type=str, 
                        help='path to nifti image directory')
    parser.add_argument('out_dir', type=str, 
                        help='path to output the corresponding tif image slices')
    parser.add_argument('-a', '--axis', type=int, default=2, 
                        help='axis of the 3d image array on which to sample the slices')
    parser.add_argument('-p', '--pct-range', nargs=2, type=float, default=(0.2,0.8),
                        help=('range of indices, as a percentage, from which to sample ' 
                              'in each 3d image volume. used to avoid creating blank tif '
                              'images if there is substantial empty space along the ends '
                              'of the chosen axis'))
    return parser


def split_filename(filepath):
    path = os.path.dirname(filepath)
    filename = os.path.basename(filepath)
    base, ext = os.path.splitext(filename)
    if ext == '.gz':
        base, ext2 = os.path.splitext(base)
        ext = ext2 + ext
    return path, base, ext


def main():
    try:
        args = arg_parser().parse_args()
        fns = glob(os.path.join(args.img_dir, '*.nii*'))
        for fn in fns:
            _, base, ext = split_filename(fn)
            img = nib.load(fn).get_data().astype(np.float32).squeeze()
            if img.ndim != 3:
                print(f'Only 3D data supported. File {base}{ext} has dimension {img.ndim}. Skipping.')
                continue
            start = int(args.pct_range[0] * img.shape[args.axis])
            end = int(args.pct_range[1] * img.shape[args.axis])
            for i in range(start, end):
                I = Image.fromarray(img[i,:,:], mode='F') if args.axis == 0 else \
                    Image.fromarray(img[:,i,:], mode='F') if args.axis == 1 else \
                    Image.fromarray(img[:,:,i], mode='F')
                I.save(os.path.join(args.out_dir, f'{base}_{i:04}.tif'))
        return 0
    except Exception as e:
        print(e)
        return 1


if __name__ == "__main__":
    sys.exit(main())

Hi @ozturkoktay, the TIFF images are probably fine. You just need to open them in a medical image viewer like MIPAV or plot them in matplotlib. This has been discussed above here and here.

Hey Jacob,
I would like to confirm that the conversion from nii to tiff is lossless. Is there a simple way to do this?

Thanks!
Brian

@mccrinbc Load your nifti image with nibabel and extract the data (.get_fdata(dtype=np.float32)). Open up all the corresponding tiff images and concatenate them along the appropriate axis. Check for equality with np.allclose(nifti_data, tiff_data))

Note that this conversion is only lossless (up to numerical precision) if the original nifti image is 32-bit floating point.

Beauty - Thanks dude!

Hello! This script is very helpful. The code worked well.
However, I have a question.
My nifti file is z-normalized. So its pixel range has negative values and doesn't go up to 255 like other image arrays.
As my purpose is to use imagenet transfer learning model (such as resnet, inception etc), I have to make input shape to 3 channel (ex; (220,220,3) ). So I did so. However when I show this 3 channel image through matoplotlib, it looks bad. If I show each channel alone, it shows fine.
Is z-normalized the problem? Thank you.

-3 channel image

-only one channel shown (image[:,:,1])

@sujeongEOM see up above (e.g., here) for a discussion of this issue.