How does it handle 3D medical volumes?

It provides patterns for reading entire directories of DICOM slices and stacking them into 3D NumPy arrays using metadata spacing information.

Can I use this for HIPAA compliance?

While the skill provides anonymization workflows for removing PHI, users should verify their specific workflows against institutional security and privacy policies.

Can it convert DICOM to PNG or JPEG?

Yes, the skill includes code patterns and specialized scripts to normalize pixel data and save it using Pillow or Matplotlib.

Does this skill handle compressed DICOM files?

Yes, it includes instructions and helper scripts for handling common compression formats like JPEG 2000 and RLE, though additional libraries like pylibjpeg may be required.

Pydicom Medical Imaging

Name: Pydicom Medical Imaging
Author: pur3v4d3r

bypur3v4d3r

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데이터 과학 및 ML

Processes, modifies, and analyzes DICOM medical imaging files using the pydicom library.

소개

This skill enables Claude to handle DICOM (Digital Imaging and Communications in Medicine) files, the industry standard for medical imaging data like CT, MRI, and X-rays. It provides structured workflows for reading metadata, extracting pixel arrays for visualization with NumPy and Matplotlib, anonymizing patient data for research, and converting medical scans into standard image formats like PNG or JPEG. It is essential for developers working on radiology workflows, medical AI training, or PACS integration.

주요 기능

Automated medical data anonymization to remove Protected Health Information (PHI)
Extract pixel data for NumPy-based image processing and visualization
Support for compressed DICOM formats including JPEG 2000 and RLE
Batch processing of DICOM series for 3D volume reconstruction
Read and write DICOM datasets with full metadata and tag access
1 GitHub stars

사용 사례

Preparing medical datasets for machine learning and AI model training
Anonymizing patient records for compliant data sharing and research purposes
Developing radiology workflow automation and PACS software integrations

소개

주요 기능

Automated medical data anonymization to remove Protected Health Information (PHI)
Extract pixel data for NumPy-based image processing and visualization
Support for compressed DICOM formats including JPEG 2000 and RLE
Batch processing of DICOM series for 3D volume reconstruction
Read and write DICOM datasets with full metadata and tag access
1 GitHub stars

사용 사례

Preparing medical datasets for machine learning and AI model training
Anonymizing patient records for compliant data sharing and research purposes
Developing radiology workflow automation and PACS software integrations