pydicom
Python package for working with DICOM files. It allows you to read, modify, and write DICOM data in a Pythonic way. Essential for medical imaging processing, clinical data extraction, and AI in radiology.
Best use case
pydicom is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Python package for working with DICOM files. It allows you to read, modify, and write DICOM data in a Pythonic way. Essential for medical imaging processing, clinical data extraction, and AI in radiology.
Teams using pydicom should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pydicom/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pydicom Compares
| Feature / Agent | pydicom | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Python package for working with DICOM files. It allows you to read, modify, and write DICOM data in a Pythonic way. Essential for medical imaging processing, clinical data extraction, and AI in radiology.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Pydicom - Medical Imaging Standards
DICOM is more than an image; it's a rich data structure containing patient info, spatial orientation, and pixel data. Pydicom provides access to all these tags.
## When to Use
- Processing medical imaging data (CT, MRI, X-ray, ultrasound).
- Extracting patient metadata and clinical information from DICOM files.
- Building AI models for radiology that require both image and metadata.
- Converting DICOM to other formats for analysis.
- Quality assurance and compliance checking in medical imaging workflows.
## Core Principles
### Datasets as Dicts
Access tags by name (e.g., `ds.PatientName`) or ID (`ds[0x0010, 0x0010]`).
### Pixel Data
Raw pixel data is stored in `PixelData`, but should be accessed via `pixel_array` for NumPy integration.
### VR (Value Representation)
Strict typing for dates, ages, and decimals ensures data integrity.
## Quick Reference
### Standard Imports
```python
import pydicom
from pydicom.data import get_testdata_files
import matplotlib.pyplot as plt
import numpy as np
```
### Basic Patterns
```python
# 1. Read file
ds = pydicom.dcmread("scan.dcm")
# 2. Access Metadata
print(f"Patient: {ds.PatientName}, ID: {ds.PatientID}")
print(f"Modality: {ds.Modality}") # CT, MR, DX
print(f"Study Date: {ds.StudyDate}")
print(f"Slice Thickness: {ds.SliceThickness}")
# 3. Access Image
plt.imshow(ds.pixel_array, cmap="gray")
plt.title(f"{ds.Modality} - {ds.PatientName}")
```
## Critical Rules
### ✅ DO
- **Use pixel_array property** - Always access pixel data via `ds.pixel_array` rather than `ds.PixelData` for proper NumPy integration.
- **Check for missing tags** - Use `hasattr(ds, 'TagName')` before accessing optional tags.
- **Respect patient privacy** - DICOM files contain PHI (Protected Health Information). Always anonymize before sharing.
- **Handle different photometric interpretations** - Some images may be inverted or use different color spaces.
### ❌ DON'T
- **Don't modify DICOM files in place** - Always create a copy when modifying to preserve original data.
- **Don't ignore VR types** - DICOM has strict data types. Converting incorrectly can corrupt data.
- **Don't assume all DICOM files have images** - Some contain only metadata (structured reports).
## Advanced Patterns
### Working with DICOM Series
```python
import pydicom
from pathlib import Path
# Load a series of DICOM files
dicom_dir = Path("dicom_series")
files = sorted(dicom_dir.glob("*.dcm"))
# Load and stack slices
slices = [pydicom.dcmread(f) for f in files]
volume = np.stack([s.pixel_array for s in slices])
```
### Anonymization
```python
# Remove patient identifiers
ds.PatientName = "ANONYMOUS"
ds.PatientID = "000000"
ds.PatientBirthDate = ""
ds.PatientSex = ""
```
Pydicom is the foundation of medical imaging in Python, enabling researchers and clinicians to work with the rich, standardized DICOM format that powers modern radiology.Related Skills
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