[Neuroimaging] Combining binary mask to 3D volume

[Jacob Bunyamin]

Thank you!

On Tue, 14 Feb 2023 at 12:53, Christopher Markiewicz <
markiewicz at stanford.edu> wrote:

> Hi Jacob,
>
> If I'm understanding the problem correctly, you mean you have a 3D image
> (maybe T1-weighted or similar) and a 3D binary mask, and what you would
> like is the original image values where the mask is 1 and 0s where the mask
> is 0.
>
> Assuming that's correct, I might take this approach:
>
> import nibabel as nib
> import numpy as np
>
> img = nib.load(img_fname)
> mask_img = nib.load(mask_fname)
>
> data = img.get_fdata()
> mask = np.asanyarray(mask_img.dataobj) != 0
>
> # Remove data outside the mask
> data[~mask] = 0
>
> masked_img = img.__class__(data, img.affine, img.header)
> masked_img.to_filename(out_fname)
>
> I suspect nilearn has tools that would do this as well. And if your mask
> is aligned to your original image but not have the same shape and affine,
> you will need to resample your mask first before you can use the data
> arrays in this way.
>
> Best,
> Chris
>
> ________________________________________
> From: Neuroimaging <neuroimaging-bounces+markiewicz=
> stanford.edu at python.org> on behalf of Jacob Bunyamin via Neuroimaging <
> neuroimaging at python.org>
> Sent: Monday, February 13, 2023 19:50
> To: neuroimaging at python.org
> Cc: Jacob Bunyamin
> Subject: [Neuroimaging] Combining binary mask to 3D volume
>
> Good afternoon,
>
> I am trying to overlay a binary mask to 3D volume and combine them into a
> single file. Is there any way to do this?
>
> Thank you,
>
> Kind regards,
>
> Jacob
>
> --
> Jacob Bunyamin
> PhD Student
>
> Department of Neuroscience
> Central Clinical School, Monash University
> 99 Commercial Road
> Melbourne VIC 3004
> E: jacob.bunyamin at monash.edu<mailto:jacob.bunyamin at monash.edu>
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