normalize one image by a rough estimate of tissue class mean

normalize-image-by-tissue.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of an image by 
finding a tissue mean in the foreground and 
voxel-wise dividing the image by that value
Author: Jacob Reinhold
"""

import sys
from argparse import ArgumentParser
from pathlib import Path

import numpy as np
import pymedio.image as mioi
from intensity_normalization.util.io import split_filename
from intensity_normalization.util.tissue_membership import find_tissue_memberships


def main() -> int:
    parser = ArgumentParser(description="Normalize image for image processing")
    parser.add_argument("path", type=Path)
    parser.add_argument("-o", "--output-path", type=Path, default=None)
    parser.add_argument("-ot", "--output-type", type=str, default="nii")
    parser.add_argument("-t", "--tissue", type=int, default=1, choices=(0, 1, 2))
    args = parser.parse_args()
    if args.output_path is None:
        root, base, _ = split_filename(args.path)
        args.output_path = root / f"{base}_norm.{args.output_type.lstrip('.')}"
    image = mioi.Image.from_path(args.path)
    mask = image > image.mean()
    tissue_memberships = find_tissue_memberships(image, mask)
    tissue_mem = tissue_memberships[..., args.tissue]
    mean = np.average(image, weights=tissue_mem)
    normalized = image / mean
    normalized.save(args.output_path)
    return 0


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

If you use this script in an academic paper, please cite the paper:

    @inproceedings{reinhold2019evaluating,
      title={Evaluating the impact of intensity normalization on {MR} image synthesis},
      author={Reinhold, Jacob C and Dewey, Blake E and Carass, Aaron and Prince, Jerry L},
      booktitle={Medical Imaging 2019: Image Processing},
      volume={10949},
      pages={109493H},
      year={2019},
      organization={International Society for Optics and Photonics}}