dicom-anonymizer

name	dicom-anonymizer
description	Batch anonymize DICOM medical images by removing patient sensitive information (name, ID, birth date) while preserving image data for research use. Trigger when users need to de-identify medical imaging data, prepare DICOM files for research sharing, or remove PHI from radiology/scanned images.
version	1.0.0
category	Clinical
tags	[]
author	AIPOCH
license	MIT
status	Draft
risk_level	Medium
skill_type	Tool/Script
owner	AIPOCH
reviewer
last_updated	2026-02-06

DICOM Anonymizer

A clinical-grade tool for batch anonymization of DICOM medical images, removing patient identifiable information while preserving essential imaging data for research and analysis.

Overview

This skill anonymizes DICOM (Digital Imaging and Communications in Medicine) files by removing or replacing Protected Health Information (PHI) while maintaining the integrity of the medical image data. It supports batch processing of entire directories and generates audit logs for compliance documentation.

Features

• Batch Processing: Process single files or entire directories recursively
• 18 PHI Tags Anonymized: Patient name, ID, birth date, institution, physician, etc.
• Configurable Anonymization: Choose between removal, hashing, or replacement strategies
• Study Linkage Preservation: Option to maintain study/series relationships using pseudonyms
• Audit Trail: Complete logging of all anonymization actions
• HIPAA Safe Harbor Compliant: Meets de-identification standards for research use

Usage

Command Line

# Anonymize a single file
python scripts/main.py --input patient_scan.dcm --output anonymized.dcm

# Batch process a directory
python scripts/main.py --input /path/to/dicom/folder/ --output /path/to/output/ --batch

# Preserve study relationships with pseudonyms
python scripts/main.py --input scans/ --output clean/ --batch --preserve-studies

# Custom anonymization (keep age, remove birth date)
python scripts/main.py --input scan.dcm --output clean.dcm --keep-tags PatientAge

Python API

from scripts.main import DICOMAnonymizer

anonymizer = DICOMAnonymizer(preserve_studies=True)
result = anonymizer.anonymize_file("input.dcm", "output.dcm")
print(f"Tags anonymized: {len(result.anonymized_tags)}")

# Batch processing
results = anonymizer.anonymize_directory("input_folder/", "output_folder/")

Parameters