Dicom Anonymizer

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.

AIpoch@aipoch-ai

Install

openclaw skills install @aipoch-ai/dicom-anonymizer

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

bash

# 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

python

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

Parameter	Type	Default	Required	Description
`--input`, `-i`	string	-	Yes	Input DICOM file or directory path
`--output`, `-o`	string	-	Yes	Output DICOM file or directory path
`--batch`, `-b`	flag	false	No	Enable batch/directory processing
`--preserve-studies`	flag	false	No	Maintain study relationships with pseudonyms
`--keep-tags`	string	-	No	Comma-separated list of tags to preserve
`--remove-private`	flag	true	No	Remove private/unknown tags
`--audit-log`	string	-	No	Path for JSON audit log
`--overwrite`	flag	false	No	Overwrite existing output files

Anonymized DICOM Tags

The following PHI tags are anonymized by default:

Patient Information

Tag	Attribute	Action
(0010,0010)	PatientName	Removed / Replaced
(0010,0020)	PatientID	Hashed / Pseudonym
(0010,0030)	PatientBirthDate	Removed
(0010,0040)	PatientSex	Preserved (demographic research)
(0010,1010)	PatientAge	Preserved (calculated from birth date)
(0010,1020)	PatientSize	Preserved
(0010,1030)	PatientWeight	Preserved

Institution & Provider

Tag	Attribute	Action
(0008,0080)	InstitutionName	Removed
(0008,0081)	InstitutionAddress	Removed
(0008,0090)	ReferringPhysicianName	Removed
(0008,1048)	PhysiciansOfRecord	Removed
(0008,1050)	PerformingPhysicianName	Removed
(0008,1060)	NameOfPhysiciansReadingStudy	Removed
(0008,1070)	OperatorsName	Removed

Study & Series

Tag	Attribute	Action
(0008,0050)	AccessionNumber	Hashed / Removed
(0020,0010)	StudyID	Hashed (if preserve-studies)
(0020,000D)	StudyInstanceUID	Hashed (if preserve-studies)
(0020,000E)	SeriesInstanceUID	Hashed (if preserve-studies)
(0020,4000)	ImageComments	Removed

Device & Acquisition

Tag	Attribute	Action
(0018,1030)	ProtocolName	Preserved / Anonymized
(0018,1000)	DeviceSerialNumber	Removed
(0008,1010)	StationName	Removed
(0008,0018)	SOPInstanceUID	Regenerated

Output Format

Anonymized DICOM File

Original PHI tags replaced with empty values, "ANONYMOUS", or hashed pseudonyms
Image pixel data (7FE0,0010) preserved unchanged
Essential metadata (modality, dimensions, acquisition params) preserved

Audit Log JSON

json

{
  "timestamp": "2024-01-15T10:30:00Z",
  "input_file": "/path/to/original.dcm",
  "output_file": "/path/to/anonymized.dcm",
  "original_patient_id_hash": "sha256:abc123...",
  "pseudonym": "ANON_0001",
  "tags_anonymized": [
    {"tag": "(0010,0010)", "attribute": "PatientName", "action": "cleared"},
    {"tag": "(0010,0020)", "attribute": "PatientID", "action": "hashed"},
    {"tag": "(0010,0030)", "attribute": "PatientBirthDate", "action": "cleared"}
  ],
  "statistics": {
    "total_tags_processed": 150,
    "phi_tags_removed": 12,
    "private_tags_removed": 5,
    "image_data_preserved": true
  }
}

Technical Architecture

DICOM Loading: Use pydicom to read DICOM files with validation
Tag Analysis: Identify and categorize PHI-containing tags
Anonymization Engine: Apply configured anonymization rules per tag
UID Handling: Regenerate or hash UIDs to maintain/break linkage
Private Tag Removal: Strip manufacturer-specific private tags
Validation: Verify output is valid DICOM and image data intact
Audit Logging: Record all transformations for compliance

Dependencies

Python 3.9+
pydicom >= 2.3.0 (DICOM file handling)
cryptography >= 3.0 (for secure hashing, optional)

See references/requirements.txt for full dependency list.

Limitations & Warnings

⚠️ CRITICAL: This tool is designed as a helper, not a replacement for institutional review.

Burned-in Annotations: Text/annotations "burned into" the image pixels are NOT removed. Pre-process with OCR-based tools if needed.
Structured Reports: DICOM SR (Structured Reporting) files may contain PHI in narrative text that may not be fully detected.
Private Tags: Some private tags may contain PHI in non-standard formats.
Secondary Captures: Scanned documents/images may have PHI visible in the pixel data.
Always perform manual QA on output before research release.
AI Autonomous Acceptance Status: 需人工检查 (Requires Manual Review)

References

references/dicom_standard_ps3.15.pdf - DICOM Standard Part 15: Security and System Management
references/hipaa_deidentification_guide.pdf - HIPAA Safe Harbor de-identification standards
references/phi_tags.json - Complete list of PHI-related DICOM tags
references/requirements.txt - Python dependencies

Technical Difficulty: High

Complex DICOM data structures, UID management, regulatory compliance requirements, potential pixel-data PHI.

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

No hardcoded credentials or API keys
No unauthorized file system access (../)
Output does not expose sensitive information
Prompt injection protections in place
Input file paths validated (no ../ traversal)
Output directory restricted to workspace
Script execution in sandboxed environment
Error messages sanitized (no stack traces exposed)
Dependencies audited

Prerequisites