pydicom-medical-imaging

To read DICOM metadata and pixel data in Python, use the pydicom library to access file attributes and extract pixel arrays into NumPy for analysis. You can retrieve patient information, study parameters, and image dimensions directly from the DICOM object.

Yes, you can convert DICOM files to PNG images by extracting the pixel data into a NumPy array, applying modality and VOI LUTs for windowing, and saving the result as a standard image format using Pillow.

To anonymize patient data in DICOM files, use pydicom to access and remove Protected Health Information (PHI) attributes from the file's metadata. This de-identification process ensures compliance with privacy standards before sharing medical imaging data.

The best way to reconstruct 3D volumes from DICOM series is to load and stack sequential DICOM files into a 3D volumetric NumPy array using pydicom. This enables visualization of orthogonal slices from CT or MRI scans.

Yes, pydicom supports compressed DICOM transfer syntaxes when optional codec handlers like pylibjpeg, pylibjpeg-libjpeg, pylibjpeg-openjpeg, and python-gdcm are installed. These dependencies decode compressed pixel data for further processing.

Yes, you can write new DICOM files from NumPy arrays by constructing a DICOM dataset with pydicom and attaching appropriate metadata to the pixel array. This allows generating valid DICOM files for medical imaging workflows.