医疗影像处理：去除医疗影像中背景的影响2D/3D【numpy-code】| CSDN博文精选

import nibabel as nib
import numpy as np
from nilearn.image.image import _crop_img_to as crop_img_to
file = 'flair.nii.gz'
image = nib.load(file)
print(image.shape)

print(np.max(image.get_data()),np.min(image.get_data()))

import matplotlib.pyplot as plt
data = image.get_data()[:,:,52]
plt.imshow(data)

def get_slice_index(data, rtol=1e-8):

    infinity_norm = max(-data.min(), data.max())
    passes_threshold = np.logical_or(data < -rtol * infinity_norm,
                                     data > rtol * infinity_norm)  ##
    if data.ndim == 4:
        passes_threshold = np.any(passes_threshold, axis=-1)

    coords = np.array(np.where(passes_threshold))
    start = coords.min(axis=1)
    end = coords.max(axis=1) + 1

    # pad with one voxel to avoid resampling problems
    start = np.maximum(start - 1, 0)
    end = np.minimum(end + 1, data.shape[:3])

    slices = [slice(s, e) for s, e in zip(start, end)]
    return slices

def have_back(image):
    background_value=0
    tolerance=0.00001
    is_foreground = np.logical_or(image.get_data() < (background_value - tolerance),
                                  image.get_data()> (background_value + tolerance))
    foreground = np.zeros(is_foreground.shape, dtype=np.uint8)
    foreground[is_foreground] = 1
    return foreground

foreground = have_back(image)
crop = get_slice_index(foreground)
print(crop)

image_o = crop_img_to(image, crop, copy=True)

import matplotlib.pyplot as plt
data_o = image_o.get_data()[:,:,52]
plt.imshow(data_o)

def get_none_zero_region(im, margin):
    """
    get the bounding box of the non-zero region of an ND volume
    """
    input_shape = im.shape
    if(type(margin) is int ):
        margin = [margin]*len(input_shape)
    assert(len(input_shape) == len(margin))
    indxes = np.nonzero(im)
    idx_min = []
    idx_max = []
    for i in range(len(input_shape)):
        idx_min.append(indxes[i].min())
        idx_max.append(indxes[i].max())

    for i in range(len(input_shape)):
        idx_min[i] = max(idx_min[i] - margin[i], 0)
        idx_max[i] = min(idx_max[i] + margin[i], input_shape[i] - 1)
    return idx_min, idx_max

def crop_ND_volume_with_bounding_box(volume, min_idx, max_idx):
    """
    crop/extract a subregion form an nd image.
    """
    dim = len(volume.shape)
    assert(dim >= 2 and dim <= 5)
    if(dim == 2):
        output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),
                               range(min_idx[1], max_idx[1] + 1))]
    elif(dim == 3):
        output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),
                               range(min_idx[1], max_idx[1] + 1),
                               range(min_idx[2], max_idx[2] + 1))]
    elif(dim == 4):
        output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),
                               range(min_idx[1], max_idx[1] + 1),
                               range(min_idx[2], max_idx[2] + 1),
                               range(min_idx[3], max_idx[3] + 1))]
    elif(dim == 5):
        output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),
                               range(min_idx[1], max_idx[1] + 1),
                               range(min_idx[2], max_idx[2] + 1),
                               range(min_idx[3], max_idx[3] + 1),
                               range(min_idx[4], max_idx[4] + 1))]
    else:
        raise ValueError("the dimension number shoud be 2 to 5")
    return output

margin = 5 
bbmin, bbmax = get_none_zero_region(arr, margin)
bbmin, bbmax

volume = crop_ND_volume_with_bounding_box(arr, bbmin, bbmax)