Output and input distribution file in Sample-based normalization methods about intensity-normalization

[jcreinhold]

I added support for this for Nyul and LSQ. Re-install the package. Make sure your version is v2.0.1.

Fitting and using for arbitrary images is supported in the Python API. For example, you can run:

# load images
import nibabel as nib
image_paths = ["path/to/image1.nii", "path/to/image2.nii", ...]
images = [nib.load(image_path) for image_path in image_paths]

# normalize the images and save the standard histogram
from intensity_normalization.normalize.nyul import NyulNormalize
nyul_normalizer = NyulNormalize()
nyul_normalizer.fit(images)
normalized = [nyul_normalizer(image) for image in images]
nyul_normalizer.save_standard_histogram("standard_histogram.npy")

# load new images and normalize those
new_image_paths = ["path/to/another/image1.nii", "path/to/another/image2.nii", ...]
new_images = [nib.load(image_path) for image_path in new_image_paths]
normalized = [nyul_normalizer(image) for image in images]

# load the standard histogram
new_nyul_normalizer = NyulNormalize()
new_nyul_normalizer.load_standard_histogram("standard_histogram.npy")
normalized = [new_nyul_normalizer(image) for image in images]

For LSQ:

from intensity_normalization.normalize.lsq import LSQNormalize
lsq_normalizer = LSQNormalize()
lsq_normalizer.fit(images)
normalized = [lsq_normalizer(image) for image in images]
lsq_normalizer.save_standard_tissue_means("tissue_means.npy")

# reload the tissue means and use
lsq_normalizer = LSQNormalize()
lsq_normalizer.load_standard_tissue_means("tissue_means.npy")
normalized = [lsq_normalizer(image) for image in images]

RAVEL is only meant to work on a particular batch, so you need to refit it if you add new data to your batch or want to use it to normalize new data.

Similar options are added to the CLI. For nyul-normalize the relevant new options are --save-standard-histogram and --load-standard-histogram. For LSQ, --save-standard-tissue-means and --load-standard-tissue-means.

Let me know if you run into a bug. Reopen the issue if so.

[wjtan99]

Thanks a lot for your quick response.