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Zero
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import numpy as np
from skimage import transform
import pydicom
from io import BytesIO
from PIL import Image
import nibabel as nib
import SimpleITK as sitk
from skimage import measure
"""
This script contains utility functions for reading and processing different imaging modalities.
"""
CT_WINDOWS = {'abdomen': [-150, 250],
'lung': [-1000, 1000],
'pelvis': [-55, 200],
'liver': [-25, 230],
'colon': [-68, 187],
'pancreas': [-100, 200]}
def process_intensity_image(image_data, is_CT, site=None):
# process intensity-based image. If CT, apply site specific windowing
# image_data: 2D numpy array of shape (H, W)
# return: 3-channel numpy array of shape (H, W, 3) as model input
if is_CT:
# process image with windowing
if site and site in CT_WINDOWS:
window = CT_WINDOWS[site]
else:
raise ValueError(f'Please choose CT site from {CT_WINDOWS.keys()}')
lower_bound, upper_bound = window
else:
# process image with intensity range 0.5-99.5 percentile
lower_bound, upper_bound = np.percentile(
image_data[image_data > 0], 0.5
), np.percentile(image_data[image_data > 0], 99.5)
image_data_pre = np.clip(image_data, lower_bound, upper_bound)
image_data_pre = (
(image_data_pre - image_data_pre.min())
/ (image_data_pre.max() - image_data_pre.min())
* 255.0
)
# pad to square with equal padding on both sides
shape = image_data_pre.shape
if shape[0] > shape[1]:
pad = (shape[0]-shape[1])//2
pad_width = ((0,0), (pad, pad))
elif shape[0] < shape[1]:
pad = (shape[1]-shape[0])//2
pad_width = ((pad, pad), (0,0))
else:
pad_width = None
if pad_width is not None:
image_data_pre = np.pad(image_data_pre, pad_width, 'constant', constant_values=0)
# resize image to 1024x1024
image_size = 1024
resize_image = transform.resize(image_data_pre, (image_size, image_size), order=3,
mode='constant', preserve_range=True, anti_aliasing=True)
# convert to 3-channel image
resize_image = np.stack([resize_image]*3, axis=-1)
return resize_image.astype(np.uint8)
def read_dicom(image_path, is_CT, site=None):
# read dicom file and return pixel data
# dicom_file: str, path to dicom file
# is_CT: bool, whether image is CT or not
# site: str, one of CT_WINDOWS.keys()
# return: 2D numpy array of shape (H, W)
ds = pydicom.dcmread(image_path)
image_array = ds.pixel_array * ds.RescaleSlope + ds.RescaleIntercept
image_array = process_intensity_image(image_array, is_CT, site)
return image_array
def read_nifti(image_path, is_CT, slice_idx, site=None, HW_index=(0, 1), channel_idx=None):
# read nifti file and return pixel data
# image_path: str, path to nifti file
# is_CT: bool, whether image is CT or not
# slice_idx: int, slice index to read
# site: str, one of CT_WINDOWS.keys()
# HW_index: tuple, index of height and width in the image shape
# return: 2D numpy array of shape (H, W)
nii = nib.load(image_path)
image_array = nii.get_fdata()
if HW_index != (0, 1):
image_array = np.moveaxis(image_array, HW_index, (0, 1))
# get slice
if channel_idx is None:
image_array = image_array[:, :, slice_idx]
else:
image_array = image_array[:, :, slice_idx, channel_idx]
image_array = process_intensity_image(image_array, is_CT, site)
return image_array
def read_rgb(image_path):
# read RGB image and return resized pixel data
# image_path: str, path to RGB image
# return: BytesIO buffer
# read image into numpy array
image = Image.open(image_path)
image = np.array(image)
if len(image.shape) == 2:
image = np.stack([image]*3, axis=-1)
elif image.shape[2] == 4:
image = image[:,:,:3]
# pad to square with equal padding on both sides
shape = image.shape
if shape[0] > shape[1]:
pad = (shape[0]-shape[1])//2
pad_width = ((0,0), (pad, pad), (0,0))
elif shape[0] < shape[1]:
pad = (shape[1]-shape[0])//2
pad_width = ((pad, pad), (0,0), (0,0))
else:
pad_width = None
if pad_width is not None:
image = np.pad(image, pad_width, 'constant', constant_values=0)
# resize image to 1024x1024 for each channel
image_size = 1024
resize_image = np.zeros((image_size, image_size, 3), dtype=np.uint8)
for i in range(3):
resize_image[:,:,i] = transform.resize(image[:,:,i], (image_size, image_size), order=3,
mode='constant', preserve_range=True, anti_aliasing=True)
return resize_image
def get_instances(mask):
# get intances from binary mask
seg = sitk.GetImageFromArray(mask)
filled = sitk.BinaryFillhole(seg)
d = sitk.SignedMaurerDistanceMap(filled, insideIsPositive=False, squaredDistance=False, useImageSpacing=False)
ws = sitk.MorphologicalWatershed( d, markWatershedLine=False, level=1)
ws = sitk.Mask( ws, sitk.Cast(seg, ws.GetPixelID()))
ins_mask = sitk.GetArrayFromImage(ws)
# filter out instances with small area outliers
props = measure.regionprops_table(ins_mask, properties=('label', 'area'))
mean_area = np.mean(props['area'])
std_area = np.std(props['area'])
threshold = mean_area - 2*std_area - 1
ins_mask_filtered = ins_mask.copy()
for i, area in zip(props['label'], props['area']):
if area < threshold:
ins_mask_filtered[ins_mask == i] = 0
return ins_mask_filtered
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