python - SimpleITK 调整图像大小

Question

我有一套 o 3D 卷，我正在阅读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产生不同的值：

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。就像是：

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

score 8 · Accepted Answer

这样做的方法是使用具有固定/任意大小和间距的重采样函数。下面是一个代码片段，展示了这个“reference_image”空间的构造：

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)

对于交钥匙解决方案，请查看这个 Jupyter 笔记本，它说明了如何在 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）。您也可以从使用的SimpleITK 笔记本存储库中找到其他笔记本。

score 2 · Accepted Answer

根据 SimpleITK 的文档，图像重采样的过程涉及 4 个步骤：

Image - 我们重新采样的图像，在坐标系中给出；
重采样网格 - 坐标系中给定的规则网格点，将映射到坐标系；
变换 - 将点从坐标系映射到坐标系；
插值器 - 一种从图像定义的点的值中获取坐标系中任意点的强度值的方法

The following snippet is for downsampling the image preserving its coordinate system properties:

def downsamplePatient(patient_CT, resize_factor):

    original_CT = sitk.ReadImage(patient_CT,sitk.sitkInt32)
    dimension = original_CT.GetDimension()
    reference_physical_size = np.zeros(original_CT.GetDimension())
    reference_physical_size[:] = [(sz-1)*spc if sz*spc>mx  else mx for sz,spc,mx in zip(original_CT.GetSize(), original_CT.GetSpacing(), reference_physical_size)]
    
    reference_origin = original_CT.GetOrigin()
    reference_direction = original_CT.GetDirection()

    reference_size = [round(sz/resize_factor) for sz in original_CT.GetSize()] 
    reference_spacing = [ phys_sz/(sz-1) for sz,phys_sz in zip(reference_size, reference_physical_size) ]

    reference_image = sitk.Image(reference_size, original_CT.GetPixelIDValue())
    reference_image.SetOrigin(reference_origin)
    reference_image.SetSpacing(reference_spacing)
    reference_image.SetDirection(reference_direction)

    reference_center = np.array(reference_image.TransformContinuousIndexToPhysicalPoint(np.array(reference_image.GetSize())/2.0))
    
    transform = sitk.AffineTransform(dimension)
    transform.SetMatrix(original_CT.GetDirection())

    transform.SetTranslation(np.array(original_CT.GetOrigin()) - reference_origin)
  
    centering_transform = sitk.TranslationTransform(dimension)
    img_center = np.array(original_CT.TransformContinuousIndexToPhysicalPoint(np.array(original_CT.GetSize())/2.0))
    centering_transform.SetOffset(np.array(transform.GetInverse().TransformPoint(img_center) - reference_center))
    centered_transform = sitk.Transform(transform)
    centered_transform.AddTransform(centering_transform)

    # sitk.Show(sitk.Resample(original_CT, reference_image, centered_transform, sitk.sitkLinear, 0.0))
    
    return sitk.Resample(original_CT, reference_image, centered_transform, sitk.sitkLinear, 0.0)

Using the snippet above in a brain CT scan we get:

python - SimpleITK 调整图像大小

2 回答 2

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