作者：北欧森林
链接：https://www.jianshu.com/p/f82d30289d68
来源：简书，已获转载授权

本笔记来源于B站Up主: 有Li 的影像组学系列教学视频
本节(35)主要介绍: 2D超声影像组学的特征提取
　
　
视频中李博士情境再现了小白研究者可能碰到的各种技术难题，并演示了解决这些问题的思路。

1、将dicom格式的2D 超声图像转为压缩的nifti格式，将其命名为`test.nii.gz`; 勾画ROI后同样保存为压缩的nifti格式，命名为`mask.nii.gz`

2、尝试提取影像学特征：

import radiomics 
from radiomics import featureextractor
 
imageFile = "/Users/Mac/Documents/JianShuNotes/2D_Ultrasound/test.nii.gz"
maskFile = "/Users/Mac/Documents/JianShuNotes/2D_Ultrasound/mask.nii.gz"
extractor = featureextractor.RadiomicsFeatureExtractor()
featureVector = extractor.execute(imageFile, maskFile)
#print(featureVector.items())
for featureName in featureVector.keys():
    print("%s: %s" % (featureName, featureVector[featureName]))

　
出现了如下报错信息：
　
sitk::ERROR: Pixel type: vector of 8-bit unsigned integer is not supported in 3D byN3itk6simple26LabelStatisticsImageFilterE
　

3、按照提示，有可能是数据格式的问题。回到源代码，修改文件相应的格式，将其命名为 `test1.nii.gz`

import SimpleITK as sitk
import numpy as np
 
folderPath = "/Users/Mac/Documents/JianShuNotes/2D_Ultrasound/"
 
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(folderPath)
reader.SetFileNames(dicom_names)
image = reader.Execute()
 
image_arr = sitk.GetArrayFromImage(image) # Note: order:z, y, x !!
size = image.GetSize()
origin = image.GetOrigin() #order: x, y, z
spacing = image.GetSpacing() #order:x, y, z
direction = image.GetDirection()
#print(spacing) 
 
pixelType = sitk.sitkInt8 #注意这里是Int8
image_new = sitk.Image(size,pixelType)
 
#image_arr_new = image_arr[:,:,::-1] #镜像翻转操作
image_arr_new = image_arr
#print(image_arr_new.shape)
 
image_new = sitk.GetImageFromArray(image_arr_new)
image_new.SetDirection(direction)
image_new.SetSpacing(spacing)
image_new.SetOrigin(origin)
sitk.WriteImage(image_new,folderPath + "test1.nii.gz")

４、将步骤[2]中的`test.nii.gz`替换为`test1.nii.gz`，再次执行该步骤，此时又出现了报错信息：

sitk::ERROR: Input "labelImage" for "LabelStatisticsImageFilter" has dimension of 3 which does not match the primary input's dimension of 2!

5、执行 `print(image_arr_new.shape)` 后显示为：

(1, 900, 1600, 3)

Notes: 这里涉及python读入nifti文件的格式问题，其顺序为 z,y,x (对应这里的1,900,1600); 这里的3为不同的slice

6、探索这个“3”代表的意义：将每一个slice导出来，通过对比发现其中的端倪。执行代码：

image_arr_new = image_arr[:,:,:,0]
#image_arr_new = image_arr[:,:,:,1]
#image_arr_new = image_arr[:,:,:,2]
#print(image_arr_new.shape)
 
image_new = sitk.GetImageFromArray(image_arr_new)
image_new.SetDirection(direction)
image_new.SetSpacing(spacing)
image_new.SetOrigin(origin)
sitk.WriteImage(image_new,folderPath + "test-slice0.nii.gz")
#sitk.WriteImage(image_new,folderPath + "test-slice1.nii.gz")
#sitk.WriteImage(image_new,folderPath + "test-slice2.nii.gz")

7、将`test-slice0.nii.gz`，`test-slice1.nii.gz`，`test-slice2.nii.gz`在软件中打开，查看它们之间的差异(肉眼似乎看不出啥区别)

8、使用写代码的方法来探索`test-slice0.nii.gz`，`test-slice1.nii.gz`，`test-slice2.nii.gz`之间的差异(视频里李博士演示了对比前二者)

import SimpleITK as sitk
import numpy as np
 
slicer0 = sitk.ReadImage("test-slice0.nii.gz")
slicer1 = sitk.ReadImage("test-slice1.nii.gz")
 
slicer0_arr = sitk.GetArrayFromImage(slicer0)
slicer1_arr = sitk.GetArrayFromImage(slicer1)
 
comp = slicer0_arr == slicer1_arr
print(comp)

9、将差异的部分保存为影像导出

image_arr = slicer0_arr - slicer1_arr
image_arr[image_arr != 0] = 1
　
size = slicer0.GetSize()
origin = slicer0.GetOrigin() #order: x, y, z
spacing = slicer0.GetSpacing() #order:x, y, z
direction = slicer0.GetDirection()
　
image_new = sitk.GetImageFromArray(image_arr)
image_new.SetDirection(direction)
image_new.SetSpacing(spacing)
image_new.SetOrigin(origin)
sitk.WriteImage(image_new,"comp.nii.gz")

10、此时在软件中打开`comp.nii.gz`文件，查看差异(在于右下角的水印部分)(视频中是左下角的水印)。

换句话说，这3张test-slice0.nii.gz，test-slice1.nii.gz，test-slice2.nii.gz是一样的，差别仅在于右下角的水印。所以，后续的工作只需选择其中的一个即可。

11、再次查看`test-slice0.nii.gz`和mask文件，发现mask文件和源文件不对应。

mask.nii.gz是基于test.nii.gz文件画出并保存的，但是在test.nii.gz保存为test1.nii.gz时存在一个翻转(我没有搞明白这一点)，所以应重新保存一下mask文件

import SimpleITK as sitk
import numpy as np
 
folderPath = "/Users/Mac/Documents/JianShuNotes/2D_Ultrasound/"
mask = sitk.ReadImage('mask.nii.gz')
mask_arr = sitk.GetArrayFromImage(mask)
 
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(folderPath)
reader.SetFileNames(dicom_names)
image = reader.Execute()
 
image_arr = sitk.GetArrayFromImage(image) # Note: order:z, y, x !!
size = image.GetSize()
origin = image.GetOrigin() #order: x, y, z
spacing = image.GetSpacing() #order:x, y, z
direction = image.GetDirection()
 
pixelType = sitk.sitkInt8 #注意这里是Int8
image_new = sitk.Image(size,pixelType)
mask_new = sitk.Image(size,pixelType)
 
#image_arr_new = image_arr[:,:,::-1] #镜像翻转操作
image_arr_new = image_arr[:,:,:,0]
print(image_arr.shape)
 
image_new = sitk.GetImageFromArray(image_arr_new)
image_new.SetDirection(direction)
image_new.SetSpacing(spacing)
image_new.SetOrigin(origin)
 
mask_new = sitk.GetImageFromArray(mask_arr) # 视频里勘误为mask_new,其实就应该是mask_arr, print二者可以看出区别来
mask_new.SetDirection(direction)
mask_new.SetSpacing(spacing)
mask_new.SetOrigin(origin)
 
sitk.WriteImage(image_new,"test0.nii.gz")
sitk.WriteImage(mask_new,"mask0.nii.gz")

12、再次尝试提取影像组学特征(代码[2])，发现代码可以运行。

但是这里有一个隐藏的bug，就是mask文件虽然重新保存了，但是并不能和原来的位置匹配。
通过软件查看图像可以发现其中的区别。

13、再次回到原始代码里查找问题来源

import SimpleITK as sitk
import numpy as np
 
folderPath = "/Users/Mac/Documents/JianShuNotes/2D_Ultrasound/"
mask = sitk.ReadImage('mask.nii.gz')
mask_arr = sitk.GetArrayFromImage(mask)
 
#reader = sitk.ImageSeriesReader()
#dicom_names = reader.GetGDCMSeriesFileNames(folderPath)
#reader.SetFileNames(dicom_names)
#image = reader.Execute()
 
image = sitk.ReadImage('test.nii.gz')
 
image_arr = sitk.GetArrayFromImage(image) # Note: order:z, y, x !!
size = image.GetSize()
origin = image.GetOrigin() #order: x, y, z
spacing = image.GetSpacing() #order:x, y, z
direction = image.GetDirection()
 
pixelType = sitk.sitkInt8 #注意这里是Int8
image_new = sitk.Image(size,pixelType)
mask_new = sitk.Image(size,pixelType)
 
#image_arr_new = image_arr[:,:,::-1] #镜像翻转操作
image_arr_new = image_arr[:,:,:,0]
print(image_arr.shape)
 
image_new = sitk.GetImageFromArray(image_arr_new)
image_new.SetDirection(direction)
image_new.SetSpacing(spacing)
image_new.SetOrigin(origin)
 
mask_new = sitk.GetImageFromArray(mask_arr)
mask_new.SetDirection(direction)
mask_new.SetSpacing(spacing)
mask_new.SetOrigin(origin)
 
sitk.WriteImage(image_new,"test0.nii.gz")
sitk.WriteImage(mask_new,"mask0.nii.gz")

14、历尽周折，现在`test0.nii.gz`文件和`mask0.nii.gz`文件相匹配了！可以愉快地提取影像组学特征了。

import radiomics 
from radiomics import featureextractor

imageFile = "/Users/Mac/Documents/JianShuNotes/2D_Ultrasound/test0.nii.gz"
maskFile = "/Users/Mac/Documents/JianShuNotes/2D_Ultrasound/mask0.nii.gz"
extractor = featureextractor.RadiomicsFeatureExtractor()
featureVector = extractor.execute(imageFile, maskFile)
#print(featureVector.items())
for featureName in featureVector.keys():
    print("%s: %s" % (featureName, featureVector[featureName]))

1、将dicom格式的2D 超声图像转为压缩的nifti格式，将其命名为test.nii.gz; 勾画ROI后同样保存为压缩的nifti格式，命名为mask.nii.gz

2、尝试提取影像学特征：

3、按照提示，有可能是数据格式的问题。回到源代码，修改文件相应的格式，将其命名为 test1.nii.gz

４、将步骤[2]中的test.nii.gz替换为test1.nii.gz，再次执行该步骤，此时又出现了报错信息：

5、执行 print(image_arr_new.shape) 后显示为：