I have 3D image of a brain (let's call it flash) and it's currently 263 x 256 x 185. I want to resize it to be the size of another image(call it whole_brain_bravo); 256 x 256 x 176, and (hopefully) use a lanczos interpolation to resample (Image.ANTIALIAS). My (failed) attempt:
from scipy import ndimage as nd
import nibabel as nib
import numpy as np
a = nib.load('flash.hdr') # nib is what I use to load the images
b = nib.load('whole_brain_bravo.hdr')
flash = a.get_data() # Access data as array (in this case memmap)
whole = b.get_data()
downed = nd.interpolation.zoom(flash, zoom=b.shape) # This obviously doesn't work
Have you guys ever done this sort of thing on a 3D image?
From the docstring for scipy.ndimage.interpolate.zoom:
"""
zoom : float or sequence, optional
The zoom factor along the axes. If a float, `zoom` is the same for each
axis. If a sequence, `zoom` should contain one value for each axis.
"""
What is the scale factor between the two images? Is it constant across all axes (i.e. are you scaling isometrically)? In that case zoom should be a single float value. Otherwise it should be a sequence of floats, one per axis.
For example, if the physical dimensions of whole and flash can be assumed to be equal, then you could do something like this:
dsfactor = [w/float(f) for w,f in zip(whole.shape, flash.shape)]
downed = nd.interpolation.zoom(flash, zoom=dsfactor)