问题描述

我正在使用 SimpleITK

import SimpleITK as sitk
for filename in filenames:
    image = sitk.ReadImage(filename)

每个卷都有不同的大小、间距、原点和方向.此代码为不同的图像生成不同的值:

Each of the volumes has different size, spacing, origin and direction. This code yields different values for different images:

print(image.GetSize())
print(image.GetOrigin())
print(image.GetSpacing())
print(image.GetDirection())

我的问题是:如何将图像转换为具有相同的大小和间距，以便在转换为 numpy 数组时它们都具有相同的分辨率和大小.比如:

My question is: how do I transform the images to have the same size and spacing so that they all have the same resolution and size when converted to numpy arrays. Something like:

import SimpleITK as sitk
for filename in filenames:
    image = sitk.ReadImage(filename)
    image = transform(image, fixed_size, fixed_spacing)
    array = sitk.GetArrayFromImage(image)

推荐答案

做到这一点的方法是使用具有固定/任意大小和间距的 Resample 函数.下面是一个代码片段，展示了这个reference_image"空间的构造:

The way to do this is to use the Resample function with fixed/arbitrary size and spacing. Below is a code snippet showing construction of this "reference_image" space:

reference_origin = np.zeros(dimension)
reference_direction = np.identity(dimension).flatten()
reference_size = [128]*dimension # Arbitrary sizes, smallest size that yields desired results. 
reference_spacing = [ phys_sz/(sz-1) for sz,phys_sz in zip(reference_size, reference_physical_size) ]

reference_image = sitk.Image(reference_size, data[0].GetPixelIDValue())
reference_image.SetOrigin(reference_origin)
reference_image.SetSpacing(reference_spacing)
reference_image.SetDirection(reference_direction)

有关交钥匙解决方案，请查看 此 Jupyter 笔记本 说明了如何在 SimpleITK 中使用可变大小的图像进行数据增强(上面的代码来自笔记本).您也可以从 SimpleITK 笔记本存储库 中找到其他可用的笔记本.

For a turnkey solution have a look at this Jupyter notebook which illustrates how to do data augmentation with variable sized images in SimpleITK (code above is from the notebook). You may find the other notebooks from the SimpleITK notebook repository of use too.

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