In [1]:

    

import dicom
import os
import numpy
from matplotlib import pyplot, cm
%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

PathDicom = "/storage/hpc_dmytro/Kaggle/SDSB/images/bmpsamples/"
lstFilesDCM = []  # create an empty list
for dirName, subdirList, fileList in os.walk(PathDicom):
    for filename in fileList:
        if ".dcm" in filename.lower():  # check whether the file's DICOM
            lstFilesDCM.append(os.path.join(dirName,filename))


    

In [4]:

    

# Get ref file
RefDs = dicom.read_file(lstFilesDCM[0])

# Load dimensions based on the number of rows, columns, and slices (along the Z axis)
ConstPixelDims = (int(RefDs.Rows), int(RefDs.Columns), len(lstFilesDCM))

# Load spacing values (in mm)
ConstPixelSpacing = (float(RefDs.PixelSpacing[0]), float(RefDs.PixelSpacing[1]), float(RefDs.SliceThickness))


    

In [15]:

    

RefDs.PatientAge


    

    
Out[15]:





'050Y'

In [5]:

    

x = numpy.arange(0.0, (ConstPixelDims[0]+1)*ConstPixelSpacing[0], ConstPixelSpacing[0])
y = numpy.arange(0.0, (ConstPixelDims[1]+1)*ConstPixelSpacing[1], ConstPixelSpacing[1])
z = numpy.arange(0.0, (ConstPixelDims[2]+1)*ConstPixelSpacing[2], ConstPixelSpacing[2])


    

In [6]:

    

# The array is sized based on 'ConstPixelDims'
ArrayDicom = numpy.zeros(ConstPixelDims, dtype=RefDs.pixel_array.dtype)

# loop through all the DICOM files
for filenameDCM in lstFilesDCM:
    # read the file
    ds = dicom.read_file(filenameDCM)
    # store the raw image data
    ArrayDicom[:, :, lstFilesDCM.index(filenameDCM)] = ds.pixel_array


    

In [12]:

    

pyplot.figure(dpi=600)
pyplot.axes().set_aspect('equal', 'datalim')
pyplot.set_cmap(pyplot.gray())
pyplot.pcolormesh(x, y, numpy.flipud(ArrayDicom[:, :, 29]))


    

    
Out[12]:





<matplotlib.collections.QuadMesh at 0x3ee0350>






    

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