How to extract labels from a Binary Image in SimpleITK in python

1
votes

I would like to extract the labels from the 2D Binary image I get using the following code:

image2DThresh = sitk.Threshold(image2D, lower=stats.GetMinimum(), upper=127.500)
cca = sitk.ConnectedComponentImageFilter()
cca_image = cca.Execute(2D_Slice)
# Get the shape statistics of the labels using
labelStats = sitk.LabelShapeStatisticsImageFilter()

The basic idea is to find the mean intensity, area of ROI and min/max indexes of the label in the main image. What I am trying to do is binarizing the image with Threshold Filter, then running CCA on this to get all the labels. Then I use the LabelShapeStatisticsImageFilter() to get the physical attributes of every label (except label 0 of course) and check if the label meets the conditions. The problem is i am not able to get the average intensity in the main image where the label is. That is why I suggest using LabelIntensityStatisticsFilter, which however for python 2.7, SimpleITK 0.10 isn't available.

2
python-2.7itksimpleitk
Request @blowekamp to shed some light – Rick M.
I am not certain what this question is asking. Please clarify. I am assuming it only refers to the SimpleITK Python wrapping. The LabelIntensityStatisticsImageFilter certainly should be available with the recent SimpleITK 0.10.0 release ( and earlier ). Secondly, there is assumption of how to solve the problem, without clearly stating what the problem or algorithm is. – blowekamp
The question is how can I get the mean intensity of a certain label (which I get from CCA) in the original image. And the SimpleITK 0.10.0 on python 2.7 on my windows machine doesn't have LabelIntensityStatisticsImageFilter – Rick M.
Additionally, how can I get the index of the pixel in the original image where the label lies, where the pixel has the lowest intensity. – Rick M.

2 Answers

5
votes

The two filters which you may be interested in are the "LabelStatisticsImageFilter" and the "LabelIntensityStatisticsImageFilter". These are both available in SimpleITK 0.10, if not you have a distribution problem. Both filters compute the mean, but the later computes a bounding box and many more advanced statistics.

Usage would go something like this:

In [1]: import SimpleITK as sitk

In [2]: print sitk.Version()
SimpleITK Version: 0.10.0 (ITK 4.10)
Compiled: Aug 16 2016 17:21:32


In [3]: img = sitk.ReadImage("cthead1.png")

In [4]: cc = sitk.ConnectedComponent(img>100)

In [5]: stats = sitk.LabelIntensityStatisticsImageFilter()

In [6]: stats.Execute(cc,img)
Out[6]: <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'std::vector< itk::simple::Image >::value_type *' at 0x2a6b540> >

In [7]: for l in stats.GetLabels():
   ...:     print("Label: {0} -> Mean: {1} Size: {2}".format(l, stats.GetMean(l), stats.GetPhysicalSize(l)))
   ...:     
Label: 1 -> Mean: 157.494210868 Size: 3643.8348071
Label: 2 -> Mean: 151.347826087 Size: 2.86239969136
Label: 3 -> Mean: 123.75 Size: 0.497808641975
Label: 4 -> Mean: 106.0 Size: 0.248904320988
Label: 5 -> Mean: 104.0 Size: 0.124452160494
Label: 6 -> Mean: 106.0 Size: 0.124452160494
Label: 7 -> Mean: 103.0 Size: 0.124452160494
Label: 8 -> Mean: 121.5 Size: 1.49342592593
Label: 9 -> Mean: 106.0 Size: 0.124452160494

In stead of printing you could create lists of labels to preserve or to relabel to 0 (erase). The ChangeLabelImageFilter can then be used to apply this change to the label image.

The combination of thresholding, statistics, and label section is a power segmentation approach which can be used and customized for many tasks. It also serves as a starting point for more complication methods.

0
votes

So I solved the problem using numpy. I'm posting the code, may be it helps someone else in the future!

def get_label(ccaimage, label, image2D):
# labelImage is the mask for a particular label
labelImage = sitk.Threshold(ccaimage, lower=label, upper=label)
#sitk_show(labelImage)
# get image as array
labelImageArray = sitk.GetArrayFromImage(labelImage)
image2Darray = sitk.GetArrayFromImage(image2D)
# ROI_1 is the minimum in the original image where the mask is equal to label
ROI_1 = image2Darray == np.min(image2Darray[labelImageArray == label])
plt.imshow(ROI_1)
plt.show()
# ROI_2 is the mask image
ROI_2 = labelImageArray == label
plt.imshow(ROI_2)
plt.show()
# AND gives me only those pixels which satisfy both conditions.
ROI = np.logical_and(image2Darray == np.min(image2Darray[labelImageArray == label]), labelImageArray == label )
avg = np.mean(image2Darray[labelImageArray == label])
print np.min(image2Darray[labelImageArray == label])
print np.where(ROI)
plt.imshow(ROI)
plt.show()