pydicom

To extract DICOM metadata, you can read the .dcm file to access patient data, tags, and pixel data. This allows you to handle medical image analysis, manipulate metadata, and work within PACS systems and radiology workflows.

Yes, you can convert DICOM files to other image formats. By extracting the pixel data from the medical imaging files, you can save the output in alternative formats for easier viewing and sharing outside of radiology workflows.

You can anonymize DICOM patient data by reading the file, modifying the specific metadata tags containing sensitive information, and writing the updated file. This ensures compliance when handling medical imaging data.

Yes, reading and writing DICOM files supports various compression formats. It utilizes pylibjpeg and python-gdcm dependencies to access and decompress pixel data, ensuring seamless medical image analysis and manipulation.

The best way to display DICOM medical images is to extract the pixel data and render it using matplotlib. This allows you to visualize the medical imaging data directly within your Python environment for analysis.

You can manipulate pixel data by reading the DICOM file and using numpy to alter the array values. This enables advanced medical image processing, allowing you to adjust image data before saving or converting the files.