
#Load the libraries
import imageio #a python dicom and image library
import scipy.ndimage as ndi
import matplotlib.pyplot as plt #main graphing library in Python


#Load a DICOM files in an imageio object
im=imageio.imread('./data/IMG/CT-MONO2-16-brain.dcm')


#this property shape will show the dimensions of the image 
# eg. (512,512) is 512 columns and 512 rows
# note (3,512,512) would be a stack of images (like CT)
im.shape

(512, 512)


#The total number of pixel or elements in the array
im.size

262144


#minimum pixel intensity (good for mapping image display)
im.min()

-1057


#maximum pixel intensity
im.max()

1440


#this lists all the dicom meta data as a list of keys
im.meta

Dict([('PixelRepresentation', 1),
      ('StudyDate', '1999.05.05'),
      ('SOPInstanceUID',
       '2.16.840.1.113662.2.1.4519.41582.4105152.419990505.410523251'),
      ('PatientOrientation', 'L\\P '),
      ('PixelData',
       b'Data converted to numpy array, raw data removed to preserve memory'),
      ('SeriesTime', '10:52:34.530000 '),
      ('ContentDate', '1999.05.05'),
      ('RescaleSlope', 1.0),
      ('TransferSyntaxUID', '1.2.840.10008.1.2.1'),
      ('InstitutionName', '105 HOSPITAL'),
      ('InstanceNumber', 8),
      ('Columns', 512),
      ('StudyDescription', ''),
      ('ImageOrientationPatient', (1.0, 0.0, 0.0, 0.0, 1.0, 0.0)),
      ('RescaleIntercept', 0.0),
      ('SeriesInstanceUID',
       '2.16.840.1.113662.2.1.2519.21582.2990505.2105152.2381633.20'),
      ('AcquisitionTime', '10:52:34.530000 '),
      ('ContentTime', '10:52:32.510000 '),
      ('BitsAllocated', 16),
      ('Manufacturer', 'Picker International, Inc.'),
      ('SeriesNumber', 3513),
      ('ImagePositionPatient', (-119.7656, -399.7656, -280.0)),
      ('SeriesDate', '1999.05.05'),
      ('AcquisitionNumber', 3513),
      ('shape', (512, 512)),
      ('SOPClassUID', '1.2.840.10008.5.1.4.1.1.2'),
      ('HighBit', 15),
      ('StudyTime', '10:52:34.530000 '),
      ('AcquisitionDate', '1999.05.05'),
      ('SamplesPerPixel', 1),
      ('BitsStored', 16),
      ('PatientName', 'Anonymized'),
      ('Rows', 512),
      ('PixelSpacing', (0.46875, 0.46875)),
      ('sampling', (0.46875, 0.46875)),
      ('StudyInstanceUID', '2.16.840.1.113662.2.1.1519.11582.1990505.1105152'),
      ('Modality', 'CT')])


#pull a single key from the DICOM meta data
im.meta['Modality']

'CT'


#plot the image using a grayscale lookup table
plt.imshow(im,cmap='gray')
plt.show()


#this applies a threshold mask on the image by the intensity of each pixel
# a mask returns a binary value levels of 0 or 1 if the pixel is below 
# or above the threshold
mask1=im>200
plt.imshow(mask1,cmap='gray')
plt.show()


#This block copies the image to a new object
#and then sets the value of the center region a new value
im2=im
im2[200:300,200:300]=1000
plt.imshow(im2,cmap='gray')
plt.show()


#This creates a new image by rotating the original one.
#very useful for registration for longtidunal analysis
im3=ndi.rotate(im,30)
plt.imshow(im3,cmap='gray')
plt.show()

Working with DICOM Files¶

Paul Nagy¶

Objectives¶