pydicom

To read and extract metadata from a DICOM file, use the pydicom library to access the file via `pydicom.dcmread('path/to/file.dcm')`, which provides a Pythonic interface for extracting both metadata and pixel data.

The best way to anonymize patient data in medical imaging files is using Python image processing libraries like pydicom, which allows you to modify or remove identifying metadata within DICOM files for research purposes.

To convert DICOM files to standard image formats, use pydicom to extract the pixel array, then apply image processing libraries like matplotlib or pillow to export the data into common formats for further analysis.

Yes, you need numpy and matplotlib to process pixel data from DICOM files, as pydicom relies on these associated libraries for handling pixel arrays and displaying medical imaging data visually.

Yes, you can use this approach for radiology workflows and PACS systems, as the processing logic applies to tasks involving medical image analysis, PACS system integration, and healthcare imaging applications.

Limitations of using Python for DICOM image processing include dependency on associated libraries like numpy and pillow for pixel data handling, and potential performance constraints when processing large volumes of medical imaging data.