Note

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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))

    (0010, 0020) Patient ID                          LO: '4MR1'
    (0010, 0030) Patient's Birth Date                DA: ''

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)

    (0010, 0020) Patient ID                          LO: 'id'
    (0010, 0030) Patient's Birth Date                DA: '19000101'

Total running time of the script: (0 minutes 0.007 seconds)

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