data_process.py

import os
import numpy as np
import nibabel as nib
from matplotlib import pyplot as plt
from skimage.transform import resize

import os
import numpy as np
import nibabel as nib
from skimage.transform import resize

def normalize(image):
    """Basic min max scaler.
    """
    min_ = np.min(image)
    max_ = np.max(image)
    scale = max_ - min_
    image = (image - min_) / scale
    return image



def irm_min_max_preprocess(image, low_perc=1, high_perc=99):
    """Main pre-processing function used for the challenge (seems to work the best).

    Remove outliers voxels first, then min-max scale.

    Warnings
    --------
    This will not do it channel wise!!
    """

    non_zeros = image > 0
    low, high = np.percentile(image[non_zeros], [low_perc, high_perc])
    image = np.clip(image, low, high)
    image = normalize(image)
    return image


def extract_middle_slices(nifti_files, num_slices=100, target_shape=(256, 256)):
    all_slices = []
    for nifti_file in nifti_files:
        img = nib.load(nifti_file)

        data = img.get_fdata()
        data = irm_min_max_preprocess(data)

        # Calculate the indices for the middle slices
        start_slice = (data.shape[2] - num_slices) // 2
        end_slice = start_slice + num_slices

        # Initialize an empty list to store 2D slices
        slices = []

        # Iterate through the slices of the NIfTI volume
        for slice_idx in range(start_slice, end_slice):
            slice_2d = data[:, :, slice_idx]
            # Resize to the target shape (256x256)
            slice_2d = resize(slice_2d, target_shape, mode='constant', anti_aliasing=True)
            slices.append(slice_2d)

        all_slices.append(slices)

    return np.concatenate(all_slices)


def save_as_numpy_array(data, save_path):
    np.save(save_path, data)



if __name__ == "__main__":
    # Path to directory containing NIfTI files
    nifti_directory = "3d_data_path"
    nifti_files = [os.path.join(nifti_directory, f) for f in os.listdir(nifti_directory) if f.endswith('.nii.gz')]
    nifti_files = sorted(nifti_files)

    # Extract middle slices and concatenate
    middle_slices = extract_middle_slices(nifti_files)

    # Save as Numpy array
    save_path =".npy file save path"
    save_as_numpy_array(middle_slices, save_path)

    print("Middle slices saved as Numpy array.")