resize_image_lungmask_node.py

#!/usr/bin/python
#this script is used to resize the img, lung_mask, node_mask
import numpy as np
from skimage import morphology
from skimage import measure
from sklearn.cluster import KMeans
from skimage.transform import resize
from glob import glob
import matplotlib.pyplot as plt
def resize_lung(img, lung_mask, node_mask):
    # we're scaling back up to the original size of the image
    new_size = [512, 512]
    img = lung_mask * img          # apply lung mask
    # 肺部区域均值和标准差
    new_mean = np.mean(img[lung_mask > 0])
    new_std = np.std(img[lung_mask > 0])
    # 重设背景颜色，这一步不明白为什么？
    old_min = np.min(img)       # background color
    img[img == old_min] = new_mean - 1.2 * new_std   # resetting backgound color
    # normalization lung region
    img = img - new_mean
    img = img / new_std
    labels = measure.label(lung_mask)
    regions = measure.regionprops(labels)
    min_row = 512
    max_row = 0
    min_col = 512
    max_col = 0
    for prop in regions:
        B = prop.bbox
        if min_row > B[0]:
            min_row = B[0]
        if min_col > B[1]:
            min_col = B[1]
        if max_row < B[2]:
            max_row = B[2]
        if max_col < B[3]:
            max_col = B[3]
    width = max_col - min_col
    height = max_row - min_row
    # #print(width,height)
    if width > height:
        max_row = min_row + width
    else:
        max_col = min_col + height
    img = img[min_row:max_row, min_col:max_col]
    lung_mask = lung_mask[min_row:max_row, min_col:max_col]
    mean = np.mean(img)
    img = img - mean
    min = np.min(img)
    max = np.max(img)
    img = img / (max - min)
    new_img = resize(img, [512, 512])
    new_lung_mask = resize(lung_mask, [512, 512])
    new_node_mask = resize(node_mask[min_row:max_row, min_col:max_col], [512, 512])  # 肺结节mask也相应裁剪和resize
    return (new_img, new_lung_mask, new_node_mask)

all_new_imgs=[]
all_new_node_masks=[]
all_new_lung_masks=[]

for count in range(all_imgs.shape[0]):
    img=all_imgs[count]
    lung_mask=all_lung_masks[count]
    node_mask=all_node_masks[count]
    if len(np.unique(lung_mask))==2:
        new_img, new_lung_mask, new_node_mask=resize_lung(img,lung_mask,node_mask)
        all_new_imgs.append(new_img)
        all_new_lung_masks.append(new_lung_mask)
        all_new_node_masks.append(new_node_mask)
all_new_imgs=np.array(all_new_imgs)
all_new_lung_masks=np.array(all_new_lung_masks).astype(np.int8)
all_new_node_masks=np.array(all_new_node_masks).astype(np.int8)

np.save("all_new_imgs.npy",all_new_imgs)
np.save("all_new_lung_masks.npy",all_new_lung_masks)
np.save("all_new_node_masks.npy",all_new_node_masks)