Does it support 3D volume reconstruction?

Yes, the skill includes logic for sorting DICOM slices by location or instance number and stacking them into 3D NumPy volumes for analysis.

Can this skill handle compressed DICOM files?

Yes, it provides instructions for installing and using additional handlers like pylibjpeg and python-gdcm to decompress JPEG, JPEG2000, and RLE formats.

Is it possible to anonymize medical records with this skill?

Absolutely. It includes specific patterns and scripts to identify and remove or replace PHI (Protected Health Information) tags like PatientName and PatientID.

What visualization libraries are recommended?

The skill demonstrates how to use Matplotlib for displaying medical images and Pillow (PIL) for converting DICOM data into standard web formats like PNG.

Pydicom Medical Imaging

Name: Pydicom Medical Imaging
Author: x-cmd

byx-cmd

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Data Science & ML

Processes medical imaging data in the DICOM standard format for healthcare applications and radiology workflows.

About

Pydicom is a specialized skill for working with DICOM (Digital Imaging and Communications in Medicine) files, the global standard for medical imaging. It empowers developers and AI agents to programmatically read, write, and modify medical datasets from modalities like CT, MRI, and X-ray. The skill covers essential workflows including pixel data extraction into NumPy arrays, metadata manipulation, automated anonymization of patient data for research compliance, and handling various compression formats. It is an indispensable tool for anyone building healthcare applications, PACS integrations, or medical machine learning pipelines.

Key Features

8 GitHub stars
Read and write DICOM datasets with support for both tag and attribute notation
Automate the anonymization of Protected Health Information (PHI)
Handle complex transfer syntaxes including JPEG2000 and RLE compression
Extract and normalize pixel data for visualization and image processing
Support for multi-frame images and 3D volume reconstruction from DICOM series

Use Cases

Creating DICOM-to-PNG/JPG conversion services for web-based medical viewers
Developing automated medical image analysis and computer-aided diagnosis tools
Building HIPAA-compliant data pipelines for medical research and AI training

About

Key Features

8 GitHub stars
Read and write DICOM datasets with support for both tag and attribute notation
Automate the anonymization of Protected Health Information (PHI)
Handle complex transfer syntaxes including JPEG2000 and RLE compression
Extract and normalize pixel data for visualization and image processing
Support for multi-frame images and 3D volume reconstruction from DICOM series

Use Cases

Creating DICOM-to-PNG/JPG conversion services for web-based medical viewers
Developing automated medical image analysis and computer-aided diagnosis tools
Building HIPAA-compliant data pipelines for medical research and AI training