I want to build a mask of the Amygdala from the Jülich probabilistic atlas.
The whole atlas comes in a 4D nifti file and I need to take several images along the 4th axis and merge them into one 3D file.
I could turn the images seperately to numpy arrays and squeeze them togheter in one 3D matrix, but this seems kind of janky.
I have been looking for a way to do it with a nilearn built-in function but I’m starting to run in circles.
Is there a way to solve this issue with a nilearn function?
One reason I don’t want to squeeze it with numpy is that I want to threshold the probability atlas at 50% chance and then binarize it. And I feel the numpy method will mess with the values in each voxel… which a nilearn function might also do - but faster.
Thanks for any help!
Hi @Lightnjer
I’m not sure what you exactly want to achieve: something like an argmax of the Julich atlas to get a per-voxel label ?
While It is not unreasonable to work with Numpy array, you probably want to use nilearn.image.math_img to perform computations at the image level.
Also, you may wnat to use index_img to get access to components images in the probabilistic atlas, but I guess that you already knew that ?
HTH,
Bertrand
Hi!
Basically I want a binary mask of the whole amygdala to use it for datadriven rs-fMRI clustering.
The problem was that the Jülich prob atlas’ Amygdala subregions are overlapping and I wanted to ask if there is some nilearn algorithm that melts multiple volumes down into one single volume - while coping with the overlapping signals in the same voxel. But on second thought that sounds a bit difficult to do.
In the mean time I found out about nilearn.image.math_img which was very useful.
I solved the issue by thresholding every single subregion, sum them up with nilearn.image.math_img and then binarizing the image.
Thank you for your help!