PydicomSAFE

The largest open-source medical AI skills library for OpenClaw🦞.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: pydicom
description: Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
---

# Pydicom

## Overview

Pydicom is a pure Python package for working with DICOM files, the standard format for medical imaging data. This skill provides guidance on reading, writing, and manipulating DICOM files, including working with pixel data, metadata, and various compression formats.

## When to Use This Skill

Use this skill when working with:
- Medical imaging files (CT, MRI, X-ray, ultrasound, PET, etc.)
- DICOM datasets requiring metadata extraction or modification
- Pixel data extraction and image processing from medical scans
- DICOM anonymization for research or data sharing
- Converting DICOM files to standard image formats
- Compressed DICOM data requiring decompression
- DICOM sequences and structured reports
- Multi-slice volume reconstruction
- PACS (Picture Archiving and Communication System) integration

## Installation

Install pydicom and common dependencies:

```bash
uv pip install pydicom
uv pip install pillow  # For image format conversion
uv pip install numpy   # For pixel array manipulation
uv pip install matplotlib  # For visualization
```

For handling compressed DICOM files, additional packages may be needed:

```bash
uv pip install pylibjpeg pylibjpeg-libjpeg pylibjpeg-openjpeg  # JPEG compression
uv pip install python-gdcm  # Alternative compression handler
```

## Core Workflows

### Reading DICOM Files

Read a DICOM file using `pydicom.dcmread()`:

```python
import pydicom

# Read a DICOM file
ds = pydicom.dcmread('path/to/file.dcm')

# Access metadata
print(f"Patient Name: {ds.PatientName}")
print(f"Study Date: {ds.StudyDate}")
print(f"Modality: {ds.Modality}")

# Display all elements
print(ds)
```

**Key points:**
- `dcmread()` returns a `Dataset` object
- Access data elements using attribute notation (e.g., `ds.PatientName`) or tag notation (e.g., `ds[0x0010, 0x0010]`)
- Use `ds.file_meta` to access file metadata like Transfer Syntax UID
- Handle missing attributes with `getattr(ds, 'AttributeName', default_value)` or `hasattr(ds, 'AttributeName')`

### Working with Pixel Data

Extract and manipulate image data from DICOM files:

```python
import pydicom
import numpy as np
import matplotlib.pyplot as plt

# Read DICOM file
ds = pydicom.dcmread('image.dcm')

# Get pixel array (requires numpy)
pixel_array = ds.pixel_array

# Image information
print(f"Shape: {pixel_array.shape}")
print(f"Data type: {pixel_array.dtype}")
print(f"Rows: {ds.Rows}, Columns: {ds.Columns}")

# Apply windowing for display (CT/MRI)
if hasattr(ds, 'WindowCenter') and hasattr(ds, 'WindowWidth'):
    from pydicom.pixel_data_handlers.util import apply_voi_lut
    windowed_image = apply_voi_lut(pixel_array, ds)
else:
    windowed_image = pixel_array

# Display image
plt.imshow(windowed_image, cmap='gray')
plt.title(f"{ds.Modality} - {ds.StudyDescription}")
plt.axis('off')
plt.show()
```

**Working with color images:**

```python
# RGB images have shape (rows, columns, 3)
if ds.PhotometricInterpretation == 'RGB':
    rgb_image = ds.pixel_array
    plt.imshow(rgb_image)
elif ds.PhotometricInterpretation == 'YBR_FULL':
    from pydicom.pixel_data_handlers.util import convert_color_space
    rgb_image = convert_color_space(ds.pixel_array, 'YBR_FULL', 'RGB')
    plt.imshow(rgb_image)
```

**Multi-frame images (videos/series):**

```python
# For multi-frame DICOM files
if hasattr(ds, 'NumberOfFrames') and ds.NumberOfFrames > 1:
    frames = ds.pixel_array  # Shape: (num_frames, rows, columns)
    print(f"Number of frames: {frames.shape[0]}")

    # Display specific frame
    plt.imshow(frames[0], cmap='gray')
```

### Converting DICOM to Image Formats

Use the provided `dicom_to_image.py` script or convert manually:

```python
from PIL import Image
import pydicom
import numpy as np

ds = pydicom.dcmread('input.dcm')
pixel_array = ds.pixel_array

# Normalize to 0-255 range
if pixel_array.dtype != np.uint8:
    pixel_array = ((pixel_array - pixel_array.min()) /
                   (pixel_array.max() - pixel_array.min()) * 255).astype(np.uint8)

# Save as PNG
image = Image.fromarray(pixel_array)
image.save('output.png')
```

Use the script: `python scripts/dicom_to_image.py input.dcm output.png`

### Modifying Metadata

Modify DICOM data elements:

```python
import pydicom
from datetime import datetime

ds = pydicom.dcmread('input.dcm')

# Modify existing elements
ds.PatientName = "Doe^John"
ds.StudyDate = datetime.now().strftime('%Y%m%d')
ds.StudyDescription = "Modified Study"

# Add new elements
ds.SeriesNumber = 1
ds.SeriesDescription = "New Series"

# Remove elements
if hasattr(ds, 'PatientComments'):
    delattr(ds, 'PatientComments')
# Or using del
if 'PatientComments' in ds:
    del ds.PatientComments

# Save modified file
ds.save_as('modified.dcm')
```

### Anonymizing DICOM Files

Remove or replace patient identifiable information:

```python
import pydicom
from datetime import datetime

ds = pydicom.dcmread('input.dcm')

# Tags commonly containing PHI (Protected Health Information)
tags_to_anonymize = [
    'PatientName', 'PatientID', 'PatientBirthDate',
    'PatientSex', 'PatientAge', 'PatientAddress',
    'InstitutionName', 'InstitutionAddress',
    'ReferringPhysicianName', 'PerformingPhysicianName',
    'OperatorsName', 'StudyDescription', 'SeriesDescription',
]

# Remove or replace sensitive data
for tag in tags_to_anonymize:
    if hasattr(ds, tag):
        if tag in ['PatientName', 'PatientID']:
 

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.