code: def _remove_localizers_by_orientation(dicoms):
      
      orientations = []
      sorted_dicoms = {}
      # Loop overall files and build dict
      for dicom_header in dicoms:
          # Create affine matrix (http://nipy.sourceforge.net/nibabel/dicom/dicom_orientation.html#dicom-slice-affine)
          image_orient1 = numpy.array(dicom_header.ImageOrientationPatient)[0:3]
          image_orient2 = numpy.array(dicom_header.ImageOrientationPatient)[3:6]
          image_orient_combined = (image_orient1.tolist(), image_orient2.tolist())
          found_orientation = False
          for orientation in orientations:
              if numpy.allclose(image_orient_combined[0], numpy.array(orientation[0]), rtol=0.001, atol=0.001) \
                      and numpy.allclose(image_orient_combined[1], numpy.array(orientation[1]), rtol=0.001,
                                         atol=0.001):
                  sorted_dicoms[str(orientation)].append(dicom_header)
                  found_orientation = True
                  break
          if not found_orientation:
              orientations.append(image_orient_combined)
              sorted_dicoms[str(image_orient_combined)] = [dicom_header]
  
      # if there are multiple possible orientations delete orientations where there are less than 4 files
      # we don't convert anything less that that anyway
  
      if len(sorted_dicoms) > 1:
          filtered_dicoms = []
          for orientation in sorted_dicoms.keys():
              if len(sorted_dicoms[orientation]) >= 4:
                  filtered_dicoms.extend(sorted_dicoms[orientation])
          return filtered_dicoms
      else:
          return six.next(six.itervalues(sorted_dicoms))
docs: Removing localizers based on the orientation.
      This is needed as in some cases with ct data there are some localizer/projection type images that cannot
      be distiguished by the dicom headers. This is why we kick out all orientations that do not have more than 4 files
      4 is the limit anyway for converting to nifti on our case