ahmedhosny

{
  "Edges": {
    "53330552": {
      "Type": "LineSegment",
      "Vertices": [
        10212927,
        43495525
      ]
    },
    "24807479": {

ahmedhosny

import nrrd # pip install pynrrd
import nibabel as nib # pip install nibabel
import numpy as np

# load nrrd 
_nrrd = nrrd.read('/path/to/nrrd.nrrd')
data = _nrrd[0]
header = _nrrd[1]
print data.shape, header

ahmedhosny

# assumes CT image nrrds with minimum value of -1024
# assumes CT mask nrrds with 0's and 1's
# assumes the shape of each patient data (iamge and mask) are different - therefore,
# this will pad all images and masks to the size of the largest
# does not preform any interpolation to isotrpic voxels or any normalization
# only saves the image and mask, therefore the metadata and pixel spacing is lost

import nrrd # pip install pynrrd # probably better performance with sitk
import numpy as np
import glob

ahmedhosny

import numpy as np
import plotly
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
init_notebook_mode(connected=True)
from plotly import tools
import scipy.signal as signal
from scipy.interpolate import interp1d

# myX - x axis values