Resizing 3D Volumetric images

If I have 3D image data in 176 x 256 x 256 dimensions, How can I resize it to say 160 x 256 x 256?
Or any other dimension? Does anyone know of any packages that can achieve this?

Kind regards.

If you have read the image into R as a 3D array with those dimensions, and you just want to "slice off" some of the cells, you can just use R subsetting. For example:

img <- array(runif(176*256*256), dim = c(176,256,256))


Of course, if you want to resize to a new grid, you would need to resample and interpolate for voxels which do not have 1:1 relations with the original grid. There are several packages that can do this, if you are interested.

Thank you for the reply,

Slicing off was the first idea that came to mind, but I have data with different dimensions to a point that I can't slice off all the time. So I'd need to use a generic function, you've mentioned some packages that do this in R, would you mind sharing them?


@mattwarkentin I’ve found some packages in python and matlab that can do this, but haven’t found any for R... Maybe I’m missing something...


I've found a way to do this in python for the time being, I'm using this to create new files with the resized data: Here is the code in python if anyone needs this:

import numpy as np
import nibabel as nib
import itertools
import os

def resize_data(data):
    initial_size_x = data.shape[0]
    initial_size_y = data.shape[1]
    initial_size_z = data.shape[2]

    new_size_x = 176
    new_size_y = 256
    new_size_z = 256

    delta_x = initial_size_x / new_size_x
    delta_y = initial_size_y / new_size_y
    delta_z = initial_size_z / new_size_z

    new_data = np.zeros((new_size_x, new_size_y, new_size_z))

    for x, y, z in itertools.product(range(new_size_x),
        new_data[x][y][z] = data[int(x * delta_x)][int(y * delta_y)][int(z * delta_z)]

    return new_data

ad_files = os.listdir()

for file in ad_files:
    initial_data = nib.load(file).get_fdata()
    if initial_data.shape != (176, 256, 256):
        resized_data = resize_data(initial_data)
        img = nib.Nifti1Image(resized_data, np.eye(4))

The keras package has several image processing functions that allow you to resize images. image_load() accepts a target_size parameter to resize an image upon loading. image_array_resize() allows you to reshape images that have already been vectorized as an array.

1 Like

Thank you, I’ll look into it.

Sorry I was away for the weekend without internet access. Did you find a sufficient solution now? I primarily work with medical images, so I use the Python SimpleITK library (also available in R but I find it easier to use reticulate and use the Python lib).

That's okay, and yes I've found a solution in python. I see that is good to know :slight_smile: I use the neurobase package in R, but now I've shifted to python for speed, and 'nibabel' works fine, it's also much faster.


1 Like

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.