base.py 文件源码

def select_training_voxels(input_masks, threshold=2, datatype=np.float32):
    """
    Select voxels for training based on a intensity threshold

    Inputs:
        - input_masks: list containing all subject image paths for a single modality
        - threshold: minimum threshold to apply (after normalizing images with 0 mean and 1 std)

    Output:
        - rois: list where each element contains the subject binary mask for selected voxels [len(x), len(y), len(z)]
    """

    # load images and normalize their intensities
    images = [load_nii(image_name).get_data() for image_name in input_masks]
    images_norm = [(im.astype(dtype=datatype) - im[np.nonzero(im)].mean()) / im[np.nonzero(im)].std() for im in images]

    # select voxels with intensity higher than threshold
    rois = [image > threshold for image in images_norm]
    return rois