Source code for WORC.processing.preprocessing

#!/usr/bin/env python

# Copyright 2017-2020 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import SimpleITK as sitk
import pydicom
import WORC.IOparser.config_preprocessing as config_io
import os
from WORC.processing.segmentix import dilate_contour
import numpy as np


[docs]def preprocess(imagefile, config, metadata=None, mask=None): ''' Apply preprocessing to an image to prepare it for feture extration ''' # Read the config, image and if given masks and metadata config = config_io.load_config(config) image = sitk.ReadImage(imagefile) if metadata is not None: metadata = pydicom.read_file(metadata) if mask is not None: mask = sitk.ReadImage(mask) # Convert image to Hounsfield units if type is CT image_type = config['ImageFeatures']['image_type'] # NOTE: We only do this if the input is a DICOM folder if 'CT' in image_type and not os.path.isfile(imagefile): print('Converting intensity to Hounsfield units.') image = image*metadata.RescaleSlope +\ metadata.RescaleIntercept # Apply the preprocessing if config['Normalize']['ROI'] == 'Full': print('Apply z-scoring on full image.') image = sitk.Normalize(image) elif config['Normalize']['ROI'] == 'True': print('Apply scaling of image based on a Region Of Interest.') # Dilate the mask if required if config['Normalize']['ROIdilate'] == 'True': radius = config['Normalize']['ROIdilateradius'] print(f"Dilating ROI with radius {radius}.") mask = sitk.GetArrayFromImage(mask) mask = dilate_contour(mask, radius) mask = mask.astype(np.uint8) mask = sitk.GetImageFromArray(mask) if mask is None: if config['Normalize']['ROIDetermine'] == 'Provided': raise IOError('Mask input required for ROI normalization.') elif config['Normalize']['ROIDetermine'] == 'Otsu': mask = 1 - sitk.OtsuThreshold(image) else: raise IOError(f"{config['Normalize']['ROIDetermine']} is not a valid method!") else: if config['Normalize']['Method'] == 'z_score': print('Apply scaling using z-scoring based on the ROI') # Cast to float to allow proper processing image = sitk.Cast(image, 9) mask = sitk.Cast(mask, 0) LabelFilter = sitk.LabelStatisticsImageFilter() try: LabelFilter.Execute(image, mask) except RuntimeError as e: if config['General']['AssumeSameImageAndMaskMetadata']: print(f'[WORC Warning] error: {e}.') print(f'[WORC Warning] Assuming image and mask have same metadata.') mask.CopyInformation(image) LabelFilter.Execute(image, mask) else: raise RuntimeError(e) ROI_mean = LabelFilter.GetMean(1) ROI_std = LabelFilter.GetSigma(1) image = sitk.ShiftScale(image, shift=-ROI_mean, scale=1.0/ROI_std) elif config['Normalize']['Method'] == 'minmed': print('Apply scaling using the minimum and median of the ROI') image = sitk.Cast(image, 9) mask = sitk.Cast(mask, 0) LabelFilter = sitk.LabelStatisticsImageFilter() try: LabelFilter.Execute(image, mask) except RuntimeError as e: if config['General']['AssumeSameImageAndMaskMetadata']: print(f'[WORC Warning] error: {e}.') print(f'[WORC Warning] Assuming image and mask have same metadata.') mask.CopyInformation(image) LabelFilter.Execute(image, mask) else: raise RuntimeError(e) ROI_median = LabelFilter.GetMedian(1) ROI_minimum = LabelFilter.GetMinimum(1) image = sitk.ShiftScale(image, shift=-ROI_minimum, scale=0.5/ROI_median) else: print('No preprocessing was applied.') return image