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"""
====================
Anonymize DICOM data
====================
This example is a starting point to anonymize DICOM data.
It shows how to read data and replace tags: person names, patient id,
optionally remove curves and private tags, and write the results in a new file.
"""
# authors : Guillaume Lemaitre <g.lemaitre58@gmail.com>
# license : MIT
import tempfile
import pydicom
from pydicom.data import get_testdata_file
print(__doc__)
###############################################################################
# Anonymize a single file
###############################################################################
filename = get_testdata_file('MR_small.dcm')
dataset = pydicom.dcmread(filename)
data_elements = ['PatientID',
'PatientBirthDate']
for de in data_elements:
print(dataset.data_element(de))
###############################################################################
# We can define a callback function to find all tags corresponding to a person
# names inside the dataset. We can also define a callback function to remove
# curves tags.
def person_names_callback(dataset, data_element):
if data_element.VR == "PN":
data_element.value = "anonymous"
def curves_callback(dataset, data_element):
if data_element.tag.group & 0xFF00 == 0x5000:
del dataset[data_element.tag]
###############################################################################
# We can use the different callback function to iterate through the dataset but
# also some other tags such that patient ID, etc.
dataset.PatientID = "id"
dataset.walk(person_names_callback)
dataset.walk(curves_callback)
###############################################################################
# pydicom allows to remove private tags using ``remove_private_tags`` method
dataset.remove_private_tags()
###############################################################################
# Data elements of type 3 (optional) can be easily deleted using ``del`` or
# ``delattr``.
if 'OtherPatientIDs' in dataset:
delattr(dataset, 'OtherPatientIDs')
if 'OtherPatientIDsSequence' in dataset:
del dataset.OtherPatientIDsSequence
###############################################################################
# For data elements of type 2, this is possible to blank it by assigning a
# blank string.
tag = 'PatientBirthDate'
if tag in dataset:
dataset.data_element(tag).value = '19000101'
##############################################################################
# Finally, this is possible to store the image
data_elements = ['PatientID',
'PatientBirthDate']
for de in data_elements:
print(dataset.data_element(de))
output_filename = tempfile.NamedTemporaryFile().name
dataset.save_as(output_filename)
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