Read, write, and anonymize DICOM files with pydicom.
npx skills add https://github.com/ovachiever/droid-tings --skill pydicom-ovachiever
Please help me install this Agent Skill. Skill: pydicom Source: https://github.com/ovachiever/droid-tings/tree/main/skills/pydicom Command: npx skills add https://github.com/ovachiever/droid-tings --skill pydicom-ovachiever
Pydicom provides a pure Python toolkit for reading, editing, anonymizing, and converting DICOM files, enabling safe handling of medical imaging data in research and clinical contexts.
Use cases include radiology research, dataset preparation, and HIPAA-compliant data sharing.
Read a DICOM file, extract pixel data, and anonymize PHI before saving a new file.
High-intent search queries and answers about installing and using this skill.
Read DICOM files using pydicom's dataset loader to access metadata and pixel arrays. Extract pixel data via the pixel_array attribute, which automatically handles decompression for supported transfer syntaxes. This works across CT, MRI, X-ray, and ultrasound formats stored in PACS systems or local files.
Pydicom enables anonymization by modifying Dataset attributes to remove or redact personally identifiable information like patient names and IDs. Save the anonymized DICOM back to disk for HIPAA-compliant data sharing in research and clinical workflows.
Extract pixel data from DICOM files using pydicom, process windowing if needed, then convert to PNG or JPEG using Pillow. This approach preserves image quality while creating formats compatible with standard image tools and distribution systems.
Pydicom supports various transfer syntaxes including compressed formats. For external codec decompression, configure optional backends. The pixel_array attribute automatically decompresses supported formats, enabling seamless access to pixel data across different DICOM encodings.
Pydicom handles multi-frame DICOM data by accessing pixel data as multi-dimensional arrays via numpy integration. Extract individual frames or process sequences for radiology research and dataset preparation workflows involving temporal medical imaging.
Modifying DICOM metadata preserves medical imaging context—window/level settings, acquisition parameters, and clinical annotations—needed for accurate radiology workflows. Direct conversion loses this information, making in-place editing essential for research datasets and clinical data sharing.
LeonChaoXRead, extract pixels from, inspect metadata of, convert, and anonymize DICOM medical images using pydicom.
gitjfmdExtract DICOM metadata and anonymize PHI from imaging files.
galeepRead, write, and modify DICOM files and metadata in Python.
jaechang-hitsRegister 3D medical images using SimpleITK with rigid, affine, or deformable methods.
