Anonymize a DICOM

gistfile1.py

def anonymize(filename, output_filename, new_person_name="anonymous",
              new_patient_id="id", remove_curves=True, remove_private_tags=True):

    def sanitize_html(dataset):
        """Removing all potential XSS attacks embedded within the dicom file"""
        dataset.walk(sanitize_callback)
        return dataset

    def sanitize_callback(dataset, data_element):
        """Call from dataset "walk" recursive function for all data elements. """
        if data_element.VR != "OW" and data_element.VR != "SQ":
            try:
                if isinstance(data_element.value, basestring):
                    data_element.value = bleach.clean(data_element.value)
            except:
                pass
    
    # Define call-back functions for the dataset.walk() function
    def PN_callback(ds, data_element):
        """Called from the dataset "walk" recursive function for all data elements."""
        if data_element.VR == "PN":
            data_element.value = new_person_name
    def curves_callback(ds, data_element):
        """Called from the dataset "walk" recursive function for all data elements."""
        if data_element.tag.group & 0xFF00 == 0x5000:
            del ds[data_element.tag]
    
    # Load the current dicom file to 'anonymize'
    dataset = dicom.read_file(filename)
    
    # remove all html tags to prevent XSS
    dataset = sanitize_html(dataset)

    # Remove patient name and any other person names
    dataset.walk(PN_callback)
    
    # Change ID
    dataset.PatientID = new_patient_id
    
    # Remove data elements (should only do so if DICOM type 3 optional) 
    # Use general loop so easy to add more later
    # Could also have done: del ds.OtherPatientIDs, etc.
    for name in ['OtherPatientIDs', 'OtherPatientIDsSequence']:
        if name in dataset:
            delattr(dataset, name)

    # Same as above but for blanking data elements that are type 2.
    for name in ['PatientBirthDate']:
        if name in dataset:
            dataset.data_element(name).value = ''
    
    # Remove private tags if function argument says to do so. Same for curves
    if remove_private_tags:
        dataset.remove_private_tags()
    if remove_curves:
        dataset.walk(curves_callback)
        
    # write the 'anonymized' DICOM out under the new filename
    dataset.save_as(output_filename)