SimpleITK ScaleTransform understanding

Beginner Questions
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1

I want to scale a 3D image by some factor, and I was expecting that the scale factor in ScaleTransform would give me a zoomed-out image when the scale factor is smaller than 1.0. Unfortunately this is not the case. I get the inverse behavior. See example code below, and resulting image slice,

image_center = 0.5 * np.array(image.shape)[::-1]                                                                                                                                                                                                                                    
scale = sitk.ScaleTransform(3, 1.8 * np.ones(3))                                                                                 
scale.SetCenter(image_center)                                                                                                                                                                                                      
                                                                                                                                 
sitk_image = sitk.GetImageFromArray(patch)                                                                                      
sitk_image_scaled = sitk.Resample(sitk_image, scale, sitk.sitkBSpline, 0.0, useNearestNeighborExtrapolator=True)

image
image640×637 40.8 KB

Is my understanding of how ScaleTransform works wrong? If e.g. you look here, it says that ITK’s ScaleTransform should result in an enlarged image when the scale is larger than 1.0.

2

Hello @Theoretialperson,

Welcome to SimpleITK!

Your understanding of the ScaleTransform is correct, the misunderstanding is with how resampling works.

Given a point \mathbf{p}=[x,y,z] and the scaling of 1.8, it will be mapped to \mathbf{p'}=[1.8x,1.8y,1.8z]. When you are doing the resampling the new image is created as intensity(p) = intensity(p'), thus for a scale factor >1 it is a zoomed out version and for scale factors 0<s<1 it is zoomed in.

For detailed examples of resampling take a look at this Jupyter notebook.

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3

Thanks @zivy ,

Indeed I was suspicious that the problem lied in my understanding of the interpolation. When I more carefully visualise sampling the image using the scaled points, I see that when the points are scaled down (scale < 1.0) it will sample from a small area in the center of the image. While when the inverse is true, many of the points will end-up outside the image and the rest will sample a low resolution version of the original image, giving the result above.

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