pydicom

Read, write, and process DICOM medical imaging files with Python.

3|Updated Apr 17, 2026

Search Tags:#image processing #dicom #format conversion #medical imaging #data anonymization

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pydicom-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydicom
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/pydicom
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pydicom-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pydicom, numpy, matplotlib, pillow, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the handling of DICOM files, providing a Pythonic interface for reading, writing, and manipulating medical imaging data.

Core Features & Use Cases

Reading & Writing DICOM: Interact with DICOM files, including extracting and modifying metadata and pixel data.
Image Processing: Perform operations like anonymization, compression, and image format conversion.
Use Case: Utilize the Skill to anonymize patient data in DICOM files for research purposes or convert them to standard image formats for analysis.

Quick Start

To read metadata from a DICOM file, use pydicom.dcmread('path/to/file.dcm'). To display an image, you can use matplotlib or other image processing libraries to plot the pixel array.

Frequently Asked Questions about pydicom

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I read and extract metadata from a DICOM file in Python?▼

To read and extract metadata from a DICOM file, use the pydicom library to access the file via `pydicom.dcmread('path/to/file.dcm')`, which provides a Pythonic interface for extracting both metadata and pixel data.

What is the best way to anonymize patient data in medical imaging files?▼

The best way to anonymize patient data in medical imaging files is using Python image processing libraries like pydicom, which allows you to modify or remove identifying metadata within DICOM files for research purposes.

How do I convert DICOM files to standard image formats for analysis?▼

To convert DICOM files to standard image formats, use pydicom to extract the pixel array, then apply image processing libraries like matplotlib or pillow to export the data into common formats for further analysis.

Do I need numpy and matplotlib to process pixel data from DICOM files?▼

Yes, you need numpy and matplotlib to process pixel data from DICOM files, as pydicom relies on these associated libraries for handling pixel arrays and displaying medical imaging data visually.

Can I use this approach for radiology workflows and PACS systems?▼

Yes, you can use this approach for radiology workflows and PACS systems, as the processing logic applies to tasks involving medical image analysis, PACS system integration, and healthcare imaging applications.

What are the limitations of using Python for DICOM image processing?▼

Limitations of using Python for DICOM image processing include dependency on associated libraries like numpy and pillow for pixel data handling, and potential performance constraints when processing large volumes of medical imaging data.