pydicom

You can read DICOM files to access and manipulate both metadata and pixel data, allowing you to extract patient information and imaging parameters for medical image analysis.

Anonymizing DICOM files involves removing sensitive patient information from the metadata, enabling secure data sharing and research without compromising privacy. This process modifies the file to strip identifiable records.

Yes, you can convert DICOM files to other image formats. By leveraging image processing dependencies like Pillow and Matplotlib, the medical imaging pixel data is exported into standard formats for easier viewing.

Yes, this approach works with numpy for medical image processing. It leverages numpy as a core dependency to handle pixel data arrays, enabling efficient manipulation and visualization of medical imaging scans.

You visualize DICOM pixel data by reading the file to extract the pixel array, then passing that array to matplotlib to render the medical image, providing a quick visual reference for radiology workflows.