调整3D图像的大小(并重新采样) [英] Resizing a 3D image (and resampling)

问题描述

我有一个大脑的3D图像(我们称它为闪光灯)，当前为263 x 256 x 185. 256 x 256 x 176，并且(希望)使用lanczos插值进行重新采样(Image.ANTIALIAS).我的(失败的)尝试:

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

你们曾经在3D图像上做过这种事情吗?

Have you guys ever done this sort of thing on a 3D image?

推荐答案

来自scipy.ndimage.interpolate.zoom的文档字符串:

"""
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.
"""

两个图像之间的比例因子是多少?它在所有轴上都恒定吗(即，您是否按等距缩放)?在这种情况下，zoom应该是单个浮点值.否则，它应该是一系列浮点数，每个轴一个.

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.

例如，如果可以假设whole和flash的物理尺寸相等，则可以执行以下操作:

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)

这篇关于调整3D图像的大小(并重新采样)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！