英文:
How to mask a DICOM image?
问题
我想要分析从头部CT中提取的"Bone"区域。
为此，我读取了DICOM文件，并对像素值小于200的区域进行了掩蔽，用"0"来填充。
然而，因为在DICOM图像中"0"代表"水"，我不知道这是否是一种适当的方式。
英文:
I would like to analyze only "Bone" regions extracted from Head CT.
For that, I read the DICOM files and masked regions where pixel values were less than 200 by filling in "0".
However, because "0" means "water" in the DICOM image, I don't know whether this is an appropriate way or not.
import pydicom
import numpy as np
dcm_img = pydicom.dcmread("0000200.dcm")
dcm_arr = dcm_img.pixel_array
masked_arr = np.where(dcm_arr < 200, 0, dcm_arr)
答案1
得分: 3
根据你的问题，我不太清楚你想如何分析CT图像中的骨骼区域，所以很难提供一个定制的答案。一般来说，我不会将图像中的值设为零，因为正如你所说，CT图像中的每个值都与特定的组织属性相关联（而且在图像处理中，通常不将图像和掩模信息混为一谈通常是不明智的）。
相反，我可能会使用掩蔽数组，屏蔽掉所有低于骨阈值的值，如下所示：
from numpy.ma import masked_array
...
masked_arr = masked_array(data=dcm_arr, mask=dcm_arr < 200)
有了这个，你可以使用掩蔽数组提供的操作，例如masked_arr.mean()，它计算所有未被掩蔽掉的体素的平均值（这就是为什么我们屏蔽了低于阈值的值）。
或者，但非常类似，我可能会创建一个新的（常规的）Numpy数组，其中包含一个布尔掩码，标记所有位于骨阈值之上的值（例如is_bone = dcm_arr >= 200），我稍后会在分析中使用它来过滤值。
无论如何，我会尽量保持掩蔽值和实际CT体素值分开。
英文:
From your question, it is not quite clear to me how exactly you want to analyze the bone regions in your CT image, so it is hard to provide a tailored answer. Generally though, I would not set values to zero in the image, because – as you said – each value in a CT image is associated with specific tissue properties (also, very generally in image processing, it is usually not a good idea to conflate image and masking information).
Instead, I would probably work with a masked array, masking out all the values that lie below the bone threshold, like so:
from numpy.ma import masked_array
...
masked_arr = masked_array(data=dcm_arr, mask=dcm_arr < 200)
With this, you could then use the operations that a masked array provides, such as masked_arr.mean(), which calculates the mean of all voxels that have not been masked out (which is why we masked the values below the threshold).
Alternatively, but very similar, I would probably create a new (regular) Numpy array, containing a boolean mask that marks all the values that do lie above the bone threshold (e.g. is_bone = dcm_arr >= 200), which I would later use for filtering values in my analyses.
In any case, I would try to keep the mask values and the actual CT voxel values separate.
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