In [1]:

import SimpleITK as sitk

In [2]:

from myshow import myshow, myshow3d

In [3]:

img_T1 = sitk.ReadImage("Data/nac-brain-atlas-1.0/volumes/A1_grayT1.nrrd")
img_T2 = sitk.ReadImage("Data/nac-brain-atlas-1.0/volumes/A1_grayT2.nrrd")
img_labels = sitk.ReadImage("Data/nac-brain-atlas-1.0/volumes/A1_labels.nrrd")

In [4]:

sitk.Show(img_T1)

Lets find a point in one of the lateral ventricles to use as a seed for region growning.

In [5]:

pt = (110, 120, 132)

In [6]:

seg = sitk.Image(img_T1.GetSize(), sitk.sitkUInt8)
seg.CopyInformation(img_T1)
seg[pt] = 1
seg = sitk.BinaryDilate(seg, 3)

In [7]:

img_T1_255 = sitk.Cast(sitk.RescaleIntensity(img_T1), sitk.sitkUInt8)
sitk.Show(sitk.LabelOverlay(img_T1_255, seg))

In [8]:

sitk.ConnectedThreshold?

In [9]:

seg = sitk.ConfidenceConnected(img_T1, [pt], numberOfIterations=0, multiplier=2)
sitk.Show(sitk.LabelOverlay(img_T1_255, seg))

In [10]:

sitk.VectorConfidenceConnected?

In [11]:

img_multi = sitk.Compose(img_T1, img_T2)
seg = sitk.VectorConfidenceConnected(img_multi, [pt], numberOfIterations=2, multiplier=4.5)
sitk.Show(sitk.LabelOverlay(img_T1_255, seg))

In [12]:

size = img_multi.GetSize()
myshow3d(sitk.LabelOverlay(img_T1_255,seg),yslices=range(50,size[1]-50,15), zslices=range(50,size[2]-50,15), dpi=30)

In [13]:

for m in (2.5, 3.0, 3.5, 4.0):
    seg = sitk.VectorConfidenceConnected(img_multi, [pt], numberOfIterations=2, multiplier=m)
    myshow3d(sitk.LabelOverlay(img_T1_255,seg),yslices=range(50,size[1]-50,15), zslices=range(50,size[2]-50,15), dpi=30, title="VectorConfidence multi="+str(m))

In [14]:

sitk.Show(sitk.LabelOverlay(img_T1_255, seg))
sitk.Show(sitk.LaplacianRecursiveGaussian(img_T1,sigma=1.0))

In [15]:

components = (img_T1, img_T2, sitk.LaplacianRecursiveGaussian(img_T1, .5), sitk.LaplacianRecursiveGaussian(img_T1, .5))
components = [sitk.Cast(img, sitk.sitkFloat32) for img in components]
img_multi = sitk.Compose(components)

In [15]: