如果要在numpy
中将3D体积平面与2D切片逐个相乘,可以使用广播:
import numpy as np
vol = np.random.rand(10, 20, 30)
slc = np.random.rand(10, 30)
new_vol = vol * slc[:, None]
如果我在SimpleITK
中尝试类似的操作,则会收到错误消息
import SimpleITK as sitk
vol_img = sitk.GetImageFromArray(vol)
slc_img = sitk.GetImageFromArray(slc[:, None])
new_vol_img = vol_img * slc_img
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-1-7d2c0160b591> in <module>
9 vol_img = sitk.GetImageFromArray(vol)
10 slc_img = sitk.GetImageFromArray(slc[:, None])
---> 11 new_vol_img = vol_img * slc_img
~\AppData\Local\Continuum\anaconda3\lib\site-packages\SimpleITK\SimpleITK.py in __mul__(self, other)
4273 def __mul__( self, other ):
4274 if isinstance( other, Image ):
-> 4275 return Multiply( self, other )
4276 try:
4277 return Multiply( self, float(other) )
~\AppData\Local\Continuum\anaconda3\lib\site-packages\SimpleITK\SimpleITK.py in Multiply(*args)
50874
50875 """
> 50876 return _SimpleITK.Multiply(*args)
50877 class N4BiasFieldCorrectionImageFilter(ImageFilter_0):
50878 """
RuntimeError: Exception thrown in SimpleITK Multiply: C:\b\3.6-64\ITK\Modules\Core\Common\src\itkDataObject.cxx:393:
Requested region is (at least partially) outside the largest possible region.
答案 0 :(得分:1)
这不能直接在SimpleITK中完成,因为图像的概念不等同于强度阵列,它具有物理空间范围(请参见this read-the-docs explanation)。两张相乘图像的间距,原点和方向必须相同。
要执行所需的操作,您需要遍历切片,然后重新组成一个卷。
以下是示例代码,该代码正是这样做的:
import SimpleITK as sitk
img = sitk.ReadImage('training_001_ct.mha')
slc = sitk.GridSource(outputPixelType=img.GetPixelID(), size=img.GetSize()[0:2],
sigma=(0.1,0.1), gridSpacing=(20.0,20.0))
slc.SetSpacing(img.GetSpacing()[0:2])
modified_slices = []
for i in range(img.GetDepth()):
current_img_slc = img[:,:,i]
slc.SetOrigin(current_img_slc.GetOrigin())
slc.SetDirection(current_img_slc.GetDirection())
modified_slices.append(current_img_slc*slc)
sitk.Show(sitk.JoinSeries(modified_slices))
请以后在ITK discourse forum上发布问题,并使用simpleitk标签。