DicomAnonymizer

Python package to anonymize DICOM files. The anonymization answer to the standard . More information about dicom fields for anonymization can be found here.

The default behaviour of this package is to anonymize DICOM fields referenced in dicomfields.

Dicom fields are separated into different groups. Each groups will be anonymized in a different way.

Group	Action	Action definition
D_TAGS	replace	Replace with a non-zero length value that may be a dummy value and consistent with the VR**
Z_TAGS	empty	Replace with a zero length value, or a non-zero length value that may be a dummy value and consistent with the VR**
X_TAGS	delete	Completely remove the tag
U_TAGS	replace_UID	Replace all UID's random ones. Same UID will have the same replaced value
Z_D_TAGS	empty_or_replace	Replace with a non-zero length value that may be a dummy value and consistent with the VR**
X_Z_TAGS	delete_or_empty	Replace with a zero length value, or a non-zero length value that may be a dummy value and consistent with the VR**
X_D_TAGS	delete_or_replace	Replace with a non-zero length value that may be a dummy value and consistent with the VR**
X_Z_D_TAGS	delete_or_empty_or_replace	Replace with a non-zero length value that may be a dummy value and consistent with the VR**
X_Z_U_STAR_TAGS	delete_or_empty_or_replace_UID	If it's a UID, then all numbers are randomly replaced. Else, replace with a zero length value, or a non-zero length value that may be a dummy value and consistent with the VR**
ALL_TAGS		Contains all previous defined tags

How to install it?

Installation can be done via pip pip install dicom-anonymizer or conda conda install -c conda-forge dicom-anonymizer.

How to test it?

• One time set up:
  • virtual environment for this package and activate it. For example set up using virtualenv venv and activate using venv\Scripts\activate.bat (on Windows)
  • Install editable version and development requirements using pip install -e .[dev]
• Run unit test using pytest

How to build it?

These instructions rely on wheel build-package format. Install it if you have not done it already using: pip install wheel

The sources files can be packaged by using: python ./setup.py bdist_wheel

This command will generate a wheel package in dist folder which can be then installed as a python package using pip install ./dist/dicom_anonymizer-1.0.12-py2.py3-none-any.whl

On Windows, if you see a warning message './dist/dicom_anonymizer-1.0.12-py2.py3-none-any.whl' looks like a filename, but the file does not exist, this could be due to pip not being able to handle relative path (See issue pypa/pip#10808). As a work-around, change directory to dist and then install it using pip install dicom_anonymizer-1.0.12-py2.py3-none-any.whl

Installing this package will also install an executable named dicom-anonymizer. In order to use it, please refer to the next section.

How to use it?

This package allows to anonymize a selection of DICOM field (defined or overridden). The way on how the DICOM fields are anonymized can also be overridden.

• [required] InputPath = Full path to a single DICOM image or to a folder which contains dicom files
• [required] OutputPath = Full path to the anonymized DICOM image or to a folder. This folder has to exist.
• [optional] ActionName = Defined an action name that will be applied to the DICOM tag.
• [optional] Dictionary = Path to a JSON file which defines actions that will be applied on specific dicom tags (see below)

Default behaviour

You can use the default anonymization behaviour describe above.

dicom-anonymizer Input Output

Private tags

Default behavior of the dicom anonymizer is to delete private tags. But you can bypass it:

• Solution 1: Use regexp to define which private tag you want to keep/update (cf custom rules)
• Solution 2: Use dicom-anonymizer.exe option to keep all private tags : --keepPrivateTags

Custom rules

You can manually add new rules in order to have different behaviors with certain tags. This will allow you to override default rules:

Executable:

dicom-anonymizer InputFilePath OutputFilePath -t '(0x0001, 0x0001)' ActionName -t '(0x0001, 0x0005)' ActionName2

This will apply the ActionName to the tag '(0x0001, 0x0001)' and ActionName2 to '(0x0001, 0x0005)'

Note: ActionName has to be defined in actions list

Example 1: The default behavior of the patient's ID is to be replaced by an empty or null value. If you want to keep this value, then you'll have to run :

python anonymizer.py InputFilePath OutputFilePath -t '(0x0010, 0x0020)' keep

This command will override the default behavior executed on this tag and the patient's ID will be kept.

Example 2: We just want to change the study date from 20080701 to 20080000, then we'll use the regexp

python anonymizer.py InputFilePath OutputFilePath -t '(0x0008, 0x0020)' 'regexp' '0701$' '0000'

Example 3: Change the tag value with an arbitrary value

python anonymizer.py InputFilePath OutputFilePath -t '(0x0010, 0x0010)' 'replace_with_value' 'new_value'

Custom rules with dictionary file

Instead of having a big command line with several new actions, you can create your own dictionary by creating a json file dictionary.json :

{
    "(0x0002, 0x0002)": "ActionName",
    "(0x0003, 0x0003)": "ActionName",
    "(0x0004, 0x0004)": "ActionName",
    "(0x0005, 0x0005)": "ActionName"
}

Same as before, the ActionName has to be defined in the actions list.

dicom-anonymizer InputFilePath OutputFilePath --dictionary dictionary.json

If you want to use the regexp action in a dictionary:

{
    "(0x0002, 0x0002)": "ActionName",
    "(0x0008, 0x0020)": {
        "action": "regexp",
        "find": "0701$",
        "replace": "0000"
    }
}

Custom/overrides actions

Here is a small example which keeps all metadata but updates the series description by adding a suffix passed as a parameter.

import argparse
from dicomanonymizer import ALL_TAGS, anonymize, keep


def main():
    parser = argparse.ArgumentParser(add_help=True)
    parser.add_argument(
        "input",
        help="Path to the input dicom file or input directory which contains dicom files",
    )
    parser.add_argument(
        "output",
        help="Path to the output dicom file or output directory which will contains dicom files",
    )
    args = parser.parse_args()

    input_dicom_path = args.input
    output_dicom_path = args.output

    extra_anonymization_rules = {}

    # Per https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html
    # it is all right to retain only the year part of the birth date for
    # de-identification purposes.
    def set_date_to_year(dataset, tag):
        element = dataset.get(tag)
        if element is not None:
            element.value = f"{element.value[:4]}0101" # YYYYMMDD format

    # ALL_TAGS variable is defined on file dicomfields.py
    # the 'keep' method is already defined into the dicom-anonymizer
    # It will overrides the default behaviour
    for i in ALL_TAGS:
        extra_anonymization_rules[i] = keep

    extra_anonymization_rules[(0x0010, 0x0030)] = set_date_to_year # Patient's Birth Date

    # Launch the anonymization
    anonymize(
        input_dicom_path,
        output_dicom_path,
        extra_anonymization_rules,
        delete_private_tags=False,
    )


if __name__ == "__main__":
    main()

See the full application in the examples folder.

In your own file, you'll have to define:

• Your custom functions. Be careful, your functions always have in inputs a dataset and a tag
• A dictionary which map your functions to a tag

Anonymize dicom tags for a dataset

You can also anonymize dicom fields in-place for pydicom's DataSet using anonymize_dataset. See this example:

import pydicom

from dicomanonymizer import anonymize_dataset

def main():

    # Create a list of tags object that should contains id, type and value
    fields = [
        { # Replaced by Anonymized
        'id': (0x0040, 0xA123),
        'type': 'LO',
        'value': 'Annie de la Fontaine',
        },
        { # Replaced with empty value
        'id': (0x0008, 0x0050),
        'type': 'TM',
        'value': 'bar',
        },
        { # Deleted
        'id': (0x0018, 0x4000),
        'type': 'VR',
        'value': 'foo',
        }
    ]

    # Create a readable dataset for pydicom
    data = pydicom.Dataset()

    # Add each field into the dataset
    for field in fields:
        data.add_new(field['id'], field['type'], field['value'])

    anonymize_dataset(data)

if __name__ == "__main__":
    main()

See the full application in the examples folder.

For more information about the pydicom's Dataset, please refer here.

You can also add extra_anonymization_rules as above:

    anonymize_dataset(data_ds, extra_anonymization_rules, delete_private_tags=True)

Actions list

Action	Action definition
empty	Replace with a zero length value, or a non-zero length value that may be a dummy value and consistent with the VR**
delete	Completely remove the tag
keep	Do nothing on the tag
replace_UID	Replace all UID's number with a random one in order to keep consistent. Same UID will have the same replaced value
empty_or_replace	Replace with a non-zero length value that may be a dummy value and consistent with the VR**
delete_or_empty	Replace with a zero length value, or a non-zero length value that may be a dummy value and consistent with the VR**
delete_or_replace	Replace with a non-zero length value that may be a dummy value and consistent with the VR**
deleteOrEmptyOrReplace	Replace with a non-zero length value that may be a dummy value and consistent with the VR**
delete_or_empty_or_replace_UID	If it's a UID, then all numbers are randomly replaced. Else, replace with a zero length value, or a non-zero length value that may be a dummy value and consistent with the VR**
regexp	Find a value in the tag using a regexp and replace it with an arbitrary value. See the examples in this file to learn how to use.
replace_with_value	Replace the tag value with an arbitrary value. See the examples in this file to learn how to use.

** VR: Value Representation

Work originally done by Edern Haumont