SimpleITK调整图像大小

Question

我有一套使用SimpleITK阅读的3D卷

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

每个体积都有不同的大小、间距、原点和方向。此代码为不同的图像生成不同的值：

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

我的问题是:如何将图像转换为相同的大小和间距，以便它们在转换为numpy数组时都具有相同的分辨率和大小。类似于：

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

Answer 1

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，显示了这个"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，请看一下this Jupyter notebook，它演示了如何在SimpleITK中使用可变大小的图像进行数据增强(上面的代码来自notebook)。你也可以从SimpleITK notebook repository of use中找到其他笔记本电脑。

Answer 2

根据SimpleITK的文档，图像重采样过程包括4个步骤：

图像图像-我们在坐标system;

• Resampling格网中重新采样的图像-将映射到坐标system;

• Transformation的坐标系中给定的点的规则格网-将点从坐标系映射到坐标system;

• Interpolator -一种用于从

定义的点的值获取坐标系中任意点的强度值的方法

以下代码片段用于对图像进行下采样，以保留其坐标系属性：

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)

在脑部CT扫描中使用上面的片段，我们可以得到：

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