In [31]:

    

import dicom
from matplotlib import pyplot as plt, cm
import numpy as np
import cv2 

%matplotlib inline


    

In [32]:

    

plan0 = dicom.read_file("../../dicom/ProstateX-0000/1.3.6.1.4.1.14519.5.2.1.7311.5101.158323547117540061132729905711/1.3.6.1.4.1.14519.5.2.1.7311.5101.100000082759836574166944843130/000000.dcm")


    

In [33]:

    

plan0.BodyPartExamined


    

    
Out[33]:





'PROSTATE'

In [34]:

    

plan0.ImagePositionPatient


    

    
Out[34]:





['-57.679780620168', '-80.935898100698', '-22.813066698769']

In [30]:

    

plan0.Columns


    

    
Out[30]:





128

In [27]:

    

rsImage = cv2.resize(plan0.pixel_array, (64,64))


    

In [28]:

    

plt.figure(figsize=(10,10));
plt.subplot(1,2,1);
plt.imshow(plan0.pixel_array, cmap=cm.bone);
plt.title('Original')

plt.subplot(1,2,2);
plt.imshow(rsImage, cmap=cm.bone);
plt.title('Resized');


    

In [10]:

    

plan1 = dicom.read_file("../../dicom/ProstateX-0000/1.3.6.1.4.1.14519.5.2.1.7311.5101.158323547117540061132729905711/1.3.6.1.4.1.14519.5.2.1.7311.5101.100000082759836574166944843130/000001.dcm")


    

In [11]:

    

plt.imshow(plan1.pixel_array, cmap=cm.bone);


    

In [7]:

    

plan1.ImageType


    

    
Out[7]:





['ORIGINAL', 'PRIMARY', 'M', 'DIS2D']

In [13]:

    




    

    
Out[13]:





'SIEMENS'

In [14]:

    

plan1.SliceThickness


    

    
Out[14]:





'3'

In [6]:

    

plan2 = dicom.read_file("/Volumes/homes/users/anthony.reina/dicom"
                        "/Lung CT/stage1/00cba091fa4ad62cc3200a657aeb957e/"
                        "0a291d1b12b86213d813e3796f14b329.dcm")


    

In [7]:

    

plt.imshow(plan2.pixel_array, cmap=cm.bone);


    

In [16]:

    

plan2.PatientID


    

    
Out[16]:





'00cba091fa4ad62cc3200a657aeb957e'

In [3]:

    

plan3 = dicom.read_file("/Volumes/homes/users/anthony.reina/dicom/Lung CT/stage1/00cba091fa4ad62cc3200a657aeb957e/034673134cbef5ea15ff9e0c8090500a.dcm")


    

In [4]:

    

plt.imshow(plan3.pixel_array, cmap=cm.bone)


    

    
Out[4]:





<matplotlib.image.AxesImage at 0x108ef7150>






    

In [12]:

    

np.shape(plan1.pixel_array)


    

    
Out[12]:





(128, 128)

In [16]:

    

plan3.Rows


    

    
Out[16]:





512

In [ ]: