# DicomAnonymizer
Python package to anonymize DICOM files.
The anonymization answer to the standard . More information about dicom fields for anonymization can be found [here](https://dicom.nema.org/medical/dicom/current/output/html/part15.html#table_E.1-1).
The default behaviour of this package is to anonymize DICOM fields referenced in the 2023e DICOM standard. These fields are referenced in [dicomfields](dicomanonymizer/dicom_anonymization_databases/dicomfields_2023.py).
Another standard can be selected, see *Change the DICOM anonymization standard*.
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`.
# Local Development Setup
To get started with local development, follow these steps:
1. Create a Virtual Environment:
- On Windows:
```sh
virtualenv env
.\env\Scripts\activate.bat
```
- On MacOS/Linux:
```sh
python -m venv env
source env/bin/activate
```
2. Install Dependencies:
- Install an editable version of the package and the development requirements:
```sh
pip install -e .[dev]
```
3. Set Up Pre-Commit Hooks:
- Install the pre-commit hooks to ensure code quality:
```sh
pre-commit install
```
## How to test it?
To run the unit tests, use the following command:
```sh
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-1.0.13-1-py2.py3-none-any.whl`
On Windows, if you see a warning message
`'./dist/dicom_anonymizer-1.0.13-1-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 https://github.com/pypa/pip/issues/10808). As a work-around, change directory to `dist` and then install it using
`pip install dicom_anonymizer-1.0.13-1-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.
```python
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](#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**:
```python
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](#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
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
python anonymizer.py InputFilePath OutputFilePath -t '(0x0008, 0x0020)' 'regexp' '0701$' '0000'
```
Example 3: Change the tag value with an arbitrary value
```python
python anonymizer.py InputFilePath OutputFilePath -t '(0x0010, 0x0010)' 'replace_with_value' 'new_value'
```
### DICOMDIR
> DICOMDIR anonymization is not specified. It is therefore discouraged and it is recommended to regenerate new DICOMDIR files after anonymizing the original DICOM files.
DICOMDIR files can have a `(0x0004, 0x1220) Directory Record Sequence` tag that can contain patient information.
However, this tag is not part of the standard tag to anonymize set. If you still want dicom-anonymizer to anonymize it, you have to instruct it explicitly:
```python
python anonymizer.py InputFilePath OutputFilePath -t '(0x0004, 0x1220)' replace
```
## Custom rules with dictionary file
Instead of having a big command line with several new actions, you can create your own dictionary by creating a json file `dictionary.json` :
```json
{
"(0x0002, 0x0002)": "ActionName",
"(0x0003, 0x0003)": "ActionName",
"(0x0004, 0x0004)": "ActionName",
"(0x0005, 0x0005)": "ActionName"
}
```
Same as before, the `ActionName` has to be defined in the [actions list](#actions-list).
```python
dicom-anonymizer InputFilePath OutputFilePath --dictionary dictionary.json
```
If you want to use the **regexp** action in a dictionary:
```json
{
"(0x0002, 0x0002)": "ActionName",
"(0x0008, 0x0020)": {
"action": "regexp",
"find": "0701$",
"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.
```python
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()
deletePrivateTags = False
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:
```python
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](https://pydicom.github.io/pydicom/stable/reference/generated/pydicom.dataset.Dataset.html).
You can also add `extra_anonymization_rules` as above:
```python
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
# Change the DICOM anonymization standard
You can customize the DICOM standard that will be used to anonymize the dataset by giving an argument `base_rules_gen` to the function `anonymize_dicom_file` or `anonymize_dataset`.
The value should be a function returning a dict of anonymization rules. Use the function `initialize_actions` to create such dict from a anonymization database from the folder `dicomanonymizer/dicom_anonymization_databases`.
Example:
```python
from dicomanonymizer.simpledicomanonymizer import anonymize_dataset, initialize_actions
anonymize_dataset(
dataset, base_rules_gen=lambda: initialize_actions("dicomfields_2024b")
)
```
Raw data
{
"_id": null,
"home_page": "https://github.com/KitwareMedical/dicom-anonymizer",
"name": "dicom-anonymizer",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "dicom, anonymizer, medical",
"author": "Laurenn Lam",
"author_email": "laurenn.lam@kitware.com",
"download_url": "https://files.pythonhosted.org/packages/c2/93/e84cf78f2317851d9d3443b8fcb4ba95c217355250fd211f4248a05ad888/dicom_anonymizer-1.0.13.post1.tar.gz",
"platform": null,
"description": "# DicomAnonymizer\n\nPython package to anonymize DICOM files.\nThe anonymization answer to the standard . More information about dicom fields for anonymization can be found [here](https://dicom.nema.org/medical/dicom/current/output/html/part15.html#table_E.1-1).\n\nThe default behaviour of this package is to anonymize DICOM fields referenced in the 2023e DICOM standard. These fields are referenced in [dicomfields](dicomanonymizer/dicom_anonymization_databases/dicomfields_2023.py). \nAnother standard can be selected, see *Change the DICOM anonymization standard*. \n\nDicom fields are separated into different groups. Each groups will be anonymized in a different way.\n\n| Group | Action | Action definition |\n| --- | --- | --- |\n| D_TAGS | replace | Replace with a non-zero length value that may be a dummy value and consistent with the VR** |\n| 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** |\n| X_TAGS | delete | Completely remove the tag |\n| U_TAGS | replace_UID | Replace all UID's random ones. Same UID will have the same replaced value |\n| Z_D_TAGS | empty_or_replace | Replace with a non-zero length value that may be a dummy value and consistent with the VR** |\n| 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** |\n| X_D_TAGS | delete_or_replace | Replace with a non-zero length value that may be a dummy value and consistent with the VR** |\n| 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** |\n| 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**|\n| ALL_TAGS | | Contains all previous defined tags\n\n\n# How to install it?\n\nInstallation can be done via pip `pip install dicom-anonymizer` or conda `conda install -c conda-forge dicom-anonymizer`.\n\n\n# Local Development Setup\n\nTo get started with local development, follow these steps:\n\n1. Create a Virtual Environment:\n - On Windows:\n ```sh\n virtualenv env\n .\\env\\Scripts\\activate.bat\n ```\n - On MacOS/Linux:\n ```sh\n python -m venv env\n source env/bin/activate\n ```\n\n2. Install Dependencies:\n - Install an editable version of the package and the development requirements:\n ```sh\n pip install -e .[dev]\n ```\n\n3. Set Up Pre-Commit Hooks:\n - Install the pre-commit hooks to ensure code quality:\n ```sh\n pre-commit install\n ```\n\n\n## How to test it?\n\nTo run the unit tests, use the following command:\n\n```sh\npytest\n```\n\n\n# How to build it?\nThese instructions rely on wheel build-package format. Install it if you have not done it already using:\n`pip install wheel`\n\nThe sources files can be packaged by using:\n`python ./setup.py bdist_wheel`\n\nThis command will generate a wheel package in `dist` folder which can be then installed as a python package using\n`pip install ./dist/dicom_anonymizer-1.0.13-1-py2.py3-none-any.whl`\n\nOn Windows, if you see a warning message\n`'./dist/dicom_anonymizer-1.0.13-1-py2.py3-none-any.whl' looks like a filename, but the file does not exist`,\nthis could be due to pip not being able to handle relative path (See issue https://github.com/pypa/pip/issues/10808). As a work-around, change directory to `dist` and then install it using\n`pip install dicom_anonymizer-1.0.13-1-py2.py3-none-any.whl`\n\n\nInstalling this package will also install an executable named `dicom-anonymizer`. In order to use it, please refer to the next section.\n\n\n\n# How to use it?\n\nThis package allows to anonymize a selection of DICOM field (defined or overridden).\nThe way on how the DICOM fields are anonymized can also be overridden.\n\n- **[required]** InputPath = Full path to a single DICOM image or to a folder which contains dicom files\n- **[required]** OutputPath = Full path to the anonymized DICOM image or to a folder. This folder has to exist.\n- [optional] ActionName = Defined an action name that will be applied to the DICOM tag.\n- [optional] Dictionary = Path to a JSON file which defines actions that will be applied on specific dicom tags (see below)\n\n\n\n## Default behaviour\n\nYou can use the default anonymization behaviour describe above.\n\n```python\ndicom-anonymizer Input Output\n```\n\n\n## Private tags\n\nDefault behavior of the dicom anonymizer is to delete private tags.\nBut you can bypass it:\n- Solution 1: Use regexp to define which private tag you want to keep/update (cf [custom rules](#custom-rules))\n- Solution 2: Use dicom-anonymizer.exe option to keep all private tags : `--keepPrivateTags`\n\n\n\n## Custom rules\nYou can manually add new rules in order to have different behaviors with certain tags.\nThis will allow you to override default rules:\n\n**Executable**:\n```python\ndicom-anonymizer InputFilePath OutputFilePath -t '(0x0001, 0x0001)' ActionName -t '(0x0001, 0x0005)' ActionName2\n```\nThis will apply the `ActionName` to the tag `'(0x0001, 0x0001)'` and `ActionName2` to `'(0x0001, 0x0005)'`\n\n**Note**: ActionName has to be defined in [actions list](#actions-list)\n\nExample 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 :\n```python\npython anonymizer.py InputFilePath OutputFilePath -t '(0x0010, 0x0020)' keep\n```\nThis command will override the default behavior executed on this tag and the patient's ID will be kept.\n\nExample 2: We just want to change the study date from 20080701 to 20080000, then we'll use the regexp\n```python\npython anonymizer.py InputFilePath OutputFilePath -t '(0x0008, 0x0020)' 'regexp' '0701$' '0000'\n```\n\nExample 3: Change the tag value with an arbitrary value\n```python\npython anonymizer.py InputFilePath OutputFilePath -t '(0x0010, 0x0010)' 'replace_with_value' 'new_value'\n```\n\n### DICOMDIR\n\n> DICOMDIR anonymization is not specified. It is therefore discouraged and it is recommended to regenerate new DICOMDIR files after anonymizing the original DICOM files.\n\nDICOMDIR files can have a `(0x0004, 0x1220) Directory Record Sequence` tag that can contain patient information. \nHowever, this tag is not part of the standard tag to anonymize set. If you still want dicom-anonymizer to anonymize it, you have to instruct it explicitly:\n\n```python\npython anonymizer.py InputFilePath OutputFilePath -t '(0x0004, 0x1220)' replace\n```\n\n## Custom rules with dictionary file\n\nInstead of having a big command line with several new actions, you can create your own dictionary by creating a json file `dictionary.json` :\n```json\n{\n \"(0x0002, 0x0002)\": \"ActionName\",\n \"(0x0003, 0x0003)\": \"ActionName\",\n \"(0x0004, 0x0004)\": \"ActionName\",\n \"(0x0005, 0x0005)\": \"ActionName\"\n}\n```\nSame as before, the `ActionName` has to be defined in the [actions list](#actions-list).\n\n```python\ndicom-anonymizer InputFilePath OutputFilePath --dictionary dictionary.json\n```\n\nIf you want to use the **regexp** action in a dictionary:\n```json\n{\n \"(0x0002, 0x0002)\": \"ActionName\",\n \"(0x0008, 0x0020)\": {\n \"action\": \"regexp\",\n \"find\": \"0701$\",\n \"replace\": \"0000\"\n }\n}\n```\n\n## Custom/overrides actions\n\nHere is a small example which keeps all metadata but updates the series description\nby adding a suffix passed as a parameter.\n\n```python\nimport argparse\nfrom dicomanonymizer import ALL_TAGS, anonymize, keep\n\n\ndef main():\n parser = argparse.ArgumentParser(add_help=True)\n parser.add_argument(\n \"input\",\n help=\"Path to the input dicom file or input directory which contains dicom files\",\n )\n parser.add_argument(\n \"output\",\n help=\"Path to the output dicom file or output directory which will contains dicom files\",\n )\n args = parser.parse_args()\n\n deletePrivateTags = False\n\n input_dicom_path = args.input\n output_dicom_path = args.output\n\n extra_anonymization_rules = {}\n\n # Per https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html\n # it is all right to retain only the year part of the birth date for\n # de-identification purposes.\n def set_date_to_year(dataset, tag):\n element = dataset.get(tag)\n if element is not None:\n element.value = f\"{element.value[:4]}0101\" # YYYYMMDD format\n\n # ALL_TAGS variable is defined on file dicomfields.py\n # the 'keep' method is already defined into the dicom-anonymizer\n # It will overrides the default behaviour\n for i in ALL_TAGS:\n extra_anonymization_rules[i] = keep\n\n extra_anonymization_rules[(0x0010, 0x0030)] = set_date_to_year # Patient's Birth Date\n\n # Launch the anonymization\n anonymize(\n input_dicom_path,\n output_dicom_path,\n extra_anonymization_rules,\n delete_private_tags=False,\n )\n\n\nif __name__ == \"__main__\":\n main()\n```\n\nSee the full application in the `examples` folder.\n\nIn your own file, you'll have to define:\n- Your custom functions. Be careful, your functions always have in inputs a dataset and a tag\n- A dictionary which map your functions to a tag\n\n## Anonymize dicom tags for a dataset\n\nYou can also anonymize dicom fields in-place for pydicom's DataSet using `anonymize_dataset`. See this example:\n```python\nimport pydicom\n\nfrom dicomanonymizer import anonymize_dataset\n\ndef main():\n\n # Create a list of tags object that should contains id, type and value\n fields = [\n { # Replaced by Anonymized\n 'id': (0x0040, 0xA123),\n 'type': 'LO',\n 'value': 'Annie de la Fontaine',\n },\n { # Replaced with empty value\n 'id': (0x0008, 0x0050),\n 'type': 'TM',\n 'value': 'bar',\n },\n { # Deleted\n 'id': (0x0018, 0x4000),\n 'type': 'VR',\n 'value': 'foo',\n }\n ]\n\n # Create a readable dataset for pydicom\n data = pydicom.Dataset()\n\n # Add each field into the dataset\n for field in fields:\n data.add_new(field['id'], field['type'], field['value'])\n\n anonymize_dataset(data)\n\nif __name__ == \"__main__\":\n main()\n```\n\nSee the full application in the `examples` folder.\n\nFor more information about the pydicom's Dataset, please refer [here](https://pydicom.github.io/pydicom/stable/reference/generated/pydicom.dataset.Dataset.html).\n\nYou can also add `extra_anonymization_rules` as above:\n```python\n anonymize_dataset(data_ds, extra_anonymization_rules, delete_private_tags=True)\n```\n\n# Actions list\n\n| Action | Action definition |\n| --- | --- |\n| empty | Replace with a zero length value, or a non-zero length value that may be a dummy value and consistent with the VR** |\n| delete | Completely remove the tag |\n| keep | Do nothing on the tag |\n| replace_UID | Replace all UID's number with a random one in order to keep consistent. Same UID will have the same replaced value |\n| empty_or_replace | Replace with a non-zero length value that may be a dummy value and consistent with the VR** |\n| 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** |\n| delete_or_replace | Replace with a non-zero length value that may be a dummy value and consistent with the VR** |\n| deleteOrEmptyOrReplace | Replace with a non-zero length value that may be a dummy value and consistent with the VR** |\n| 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** |\n|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.|\n|replace_with_value| Replace the tag value with an arbitrary value. See the examples in this file to learn how to use.\n\n\n** VR: Value Representation\n\nWork originally done by Edern Haumont\n\n# Change the DICOM anonymization standard\n\nYou can customize the DICOM standard that will be used to anonymize the dataset by giving an argument `base_rules_gen` to the function `anonymize_dicom_file` or `anonymize_dataset`. \nThe value should be a function returning a dict of anonymization rules. Use the function `initialize_actions` to create such dict from a anonymization database from the folder `dicomanonymizer/dicom_anonymization_databases`.\n\nExample:\n```python\nfrom dicomanonymizer.simpledicomanonymizer import anonymize_dataset, initialize_actions\n\nanonymize_dataset(\n dataset, base_rules_gen=lambda: initialize_actions(\"dicomfields_2024b\")\n)\n```\n",
"bugtrack_url": null,
"license": null,
"summary": "Program to anonymize dicom files with default and custom rules",
"version": "1.0.13.post1",
"project_urls": {
"Bug Tracker": "https://github.com/KitwareMedical/dicom-anonymizer/issues",
"Homepage": "https://github.com/KitwareMedical/dicom-anonymizer"
},
"split_keywords": [
"dicom",
" anonymizer",
" medical"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "7e5ec1f75bb8fd467269abc7f78abf0392ec2b7f7b303579bb1a8ea23419cdde",
"md5": "e88d1de8a357291ecd7c287845e5b264",
"sha256": "cd4ddb656f96ebb67a987d2245a9de7d871f9e6942ab807989586a3131862392"
},
"downloads": -1,
"filename": "dicom_anonymizer-1.0.13.post1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e88d1de8a357291ecd7c287845e5b264",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 29962,
"upload_time": "2024-09-17T11:24:18",
"upload_time_iso_8601": "2024-09-17T11:24:18.128718Z",
"url": "https://files.pythonhosted.org/packages/7e/5e/c1f75bb8fd467269abc7f78abf0392ec2b7f7b303579bb1a8ea23419cdde/dicom_anonymizer-1.0.13.post1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c293e84cf78f2317851d9d3443b8fcb4ba95c217355250fd211f4248a05ad888",
"md5": "8f2a27d3eeb782fdd7392884015e7035",
"sha256": "fc5763235c0115f47475acf1a84902c407ad781f5f9425ed65cd1322fb0d76f4"
},
"downloads": -1,
"filename": "dicom_anonymizer-1.0.13.post1.tar.gz",
"has_sig": false,
"md5_digest": "8f2a27d3eeb782fdd7392884015e7035",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 30928,
"upload_time": "2024-09-17T11:24:19",
"upload_time_iso_8601": "2024-09-17T11:24:19.569659Z",
"url": "https://files.pythonhosted.org/packages/c2/93/e84cf78f2317851d9d3443b8fcb4ba95c217355250fd211f4248a05ad888/dicom_anonymizer-1.0.13.post1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-17 11:24:19",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "KitwareMedical",
"github_project": "dicom-anonymizer",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "dicom-anonymizer"
}
JSON
