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preprocess_database_liver.py
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##
#Proir arthors
#Nathan Reilly
#Gregory Glatzer
#5/14/2021
#Leslie Wubbel
###
import os
import nibabel as nib # pip install nibabel # https://nipy.org/nibabel/nifti_images.html
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from PIL import Image
import time
def gen_mat_pngs_from_nifti(niftis_path, root_process_database):
start = time.time()
# path constants
# niftis_path = 'E:\Datasets\LiTS_liver_lesion\LITS17'
# root_process_database = '../../output_folder/'
## Folders to be created
folder_volumes = os.path.join(root_process_database, 'images_volumes/')
folder_seg_liver = os.path.join(root_process_database, 'liver_seg/')
folder_seg_item = os.path.join(root_process_database, 'item_seg/')
# create non-existent paths
folder_paths = [root_process_database, folder_volumes, folder_seg_liver, folder_seg_item]
for p in folder_paths:
if not os.path.exists(p):
os.mkdir(p)
# filter to only files starting with v (volume) or s (segmentation)
filenames = [filename for filename in os.listdir(niftis_path) if filename[0] in ('v', 's')]
for filename in filenames:
path_file = os.path.join(niftis_path, filename)
index = filename.find('.nii') + 1 # +1 to account for matlab --> python
if filename[0] == 'v':
print('Processing Volume {}'.format(filename))
folder_volume = os.path.join(folder_volumes, filename[7:index-1])
volume = nib.load(path_file) # load nifti
imgs = volume.get_fdata() # get 3d NumPy array
# clipping HU pixel clipping
imgs[imgs<-150] = -150
imgs[imgs>250] = 250
## # equivalent to matlab single()
imgs = imgs.astype(np.float32)
img_max, img_min = (np.max(imgs), np.min(imgs))
# create folder_volume folder
img_volume = 255*(imgs - img_min)/(img_max-img_min)
if not os.path.exists(folder_volume):
os.mkdir(folder_volume)
for k in range(img_volume.shape[2]):
section = img_volume[:,:,k]
filename_for_section = os.path.join(folder_volume, str(k+1) + '.mat')
scipy.io.savemat(filename_for_section, {'section': section})
else:
print('Processing Segmentation {}'.format(filename))
folder_seg_item_num = os.path.join(folder_seg_item, filename[13:index-1])
folder_seg_liver_num = os.path.join(folder_seg_liver, filename[13:index-1])
segmentation = nib.load(path_file)
img_seg = segmentation.get_fdata().astype(np.uint8)
print(img_seg.shape)
# binarize and normalize data
img_seg_item = img_seg.copy()
img_seg_liver = img_seg.copy()
# create masks
img_seg_item[img_seg_item == 1] = 0
img_seg_item[img_seg_item == 2] = 1
img_seg_liver[img_seg_liver == 2] = 1
# create dirs
if not os.path.exists(folder_seg_item_num):
os.mkdir(folder_seg_item_num)
if not os.path.exists(folder_seg_liver_num):
os.mkdir(folder_seg_liver_num)
# save images
for k in range(0, img_seg_item.shape[2]):
print(filename, ", ", k)
# item
item_seg_section = np.fliplr(np.flipud(img_seg_item[:,:,k]*255)) # flip on both axes
item_seg_filename = os.path.join(folder_seg_item_num, str(k+1) + '.png')
im_item = Image.fromarray(item_seg_section)
im_item.save(item_seg_filename)
# liver
liver_seg_section = np.fliplr(np.flipud(img_seg_liver[:,:,k]*255))
liver_seg_filename = os.path.join(folder_seg_liver_num, str(k+1) + '.png')
im_liver = Image.fromarray(liver_seg_section)
im_liver.save(liver_seg_filename)
end = time.time()
print("Elapsed Time is:", end - start)