# Detecting planes in binary images

I’m trying to detect certain type of artifacts in binary volumes I’m working with, namely step-like transitions between 0 and 1, but only in the three orthogonal planes XY/XZ/YZ. Example:

The surface is generally rough and doesn’t end sharply, but in this case it’s truncated in Z direction (the volume is rotated in the screenshot).

What ITK filters can I use to do this efficiently? I don’t need to segment those edges, I just need to know whether they exist in the volume. I would also need to somehow control the size threshold: it’s fine if a 2x2 voxel area vanishes abruplty, but a 20x20 step is something I want to know about.

A simple idea I tried is to convolve an edge-detecting kernel and threshold the output’s absolute intensity (this one is for detecting edges in Z direction only):

``````    kernel_np = np.zeros((2, 20, 20), dtype=np.int16)
kernel_np[0, :, :] = -1
kernel_np[1, :, :] = 1
kernel_sitk = sitk.GetImageFromArray(kernel_np)

output = sitk.Convolution(input, kernel_sitk)
output = sitk.Abs(output)
output = sitk.IntensityWindowing(output, 200.0)
``````

I think I can get it to work with some fine tuning, but it’s quite slow. The binary volumes I’m analysing are of the order of 500x500x500.

That is a huge kernel for convolution. Try a smaller one.

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Good idea. I was thinking that I could filter out smaller flat areas by making the kernel big, but that’s obviously inefficient.

Can I use the fact that the volumes I’m working with are sparse (>90% zeros) or that they’re all binary?

Most label volumes are sparse, and many are binary. Not easy to take advantage of that in this case. Maybe check whether any of the morphological filters help you somehow?

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