python - 如何对使用 SimpleITK 读取的 DICOM 图像进行直方图均衡化

Question

我正在尝试对从 *.nii.gz 文件读取的所有图像进行直方图均衡。

我尝试过这段代码:

import SimpleITK as sitk
flair_file = '/content/gdrive/My Drive/Colab Notebooks/.../FLAIR.nii.gz'

images = sitk.ReadImage(flair_file)
print("Width: ", images.GetWidth())
print("Height:", images.GetHeight())
print("Depth: ", images.GetDepth())

print("Dimension:", images.GetDimension())
print("Pixel ID: ", images.GetPixelIDValue())
print("Pixel ID Type:", images.GetPixelIDTypeAsString())

有了这个输出:

Width:  240
Height: 240
Depth:  48
Dimension: 3
Pixel ID:  8
Pixel ID Type: 32-bit float

但是当我尝试使用 OpenCV 进行直方图均衡时，出现错误:

images_array = sitk.GetArrayFromImage(images)
gray = cv2.cvtColor(images_array[24], cv2.COLOR_BGR2GRAY)

输出:

error: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/color.simd_helpers.hpp:92: error: (-2:Unspecified error) in function 'cv::impl::{anonymous}::CvtHelper<VScn, VDcn, VDepth, sizePolicy>::CvtHelper(cv::InputArray, cv::OutputArray, int) [with VScn = cv::impl::{anonymous}::Set<3, 4>; VDcn = cv::impl::{anonymous}::Set<1>; VDepth = cv::impl::{anonymous}::Set<0, 2, 5>; cv::impl::{anonymous}::SizePolicy sizePolicy = (cv::impl::<unnamed>::SizePolicy)2u; cv::InputArray = const cv::_InputArray&; cv::OutputArray = const cv::_OutputArray&]'
> Invalid number of channels in input image:
>     'VScn::contains(scn)'
> where
>     'scn' is 1

所以，我尝试了其他代码:

images_array = sitk.GetArrayFromImage(images)
#gray = cv2.cvtColor(images_array[24], cv2.COLOR_BGR2GRAY)
output = cv2.equalizeHist(images_array[24])

但我收到此错误:

error: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/histogram.cpp:3429: error: (-215:Assertion failed) _src.type() == CV_8UC1 in function 'equalizeHist'

如何对这些 DICOM 图像进行直方图均衡(也许不使用 OpenCV，而是使用 SimpleITK)？

更新:
当我运行此命令时:

print(images_array[24].shape, images_array[24].dtype)

我明白了:

(240, 240) float32

Answer 1

SimpleITK 确实有一个 AdaptiveHistogramEqualization 函数，并且它确实适用于 float32 图像。以下是如何使用它:

new_images = sitk.AdaptiveHistogramEqualization(images)

请注意，这将对整个 3D 图像进行均衡。如果你想逐个切片地进行，它看起来像这样:

new_images = []
for z in range(images.GetDepth()):
    new_images.append(sitk.AdaptiveHistogramEqualization(images[:,:,z])

更新:正如 @blowekamp 所指出的，AHE 不会在整个图像上产生全局直方图均衡，而是产生局部均衡。下面是一些示例代码，展示了如何使用他所描述的 HistogramMatching 函数来实现全局直方图均衡。

import SimpleITK as sitk
import numpy as np

# Create a noise Gaussian blob test image
img = sitk.GaussianSource(sitk.sitkFloat32, size=[240,240,48], mean=[120,120,24])
img = img + sitk.AdditiveGaussianNoise(img,10)

# Create a ramp image of the same size
h = np.arange(0.0, 255,1.0666666666, dtype='f4')
h2 = np.reshape(np.repeat(h, 240*48), (48,240,240))
himg = sitk.GetImageFromArray(h2)
print(himg.GetSize())

# Match the histogram of the Gaussian image with the ramp
result=sitk.HistogramMatching(img, himg)

# Display the 3d image
import itkwidgets
itkwidgets.view(result)

请注意，itkwidgets 允许您在 Jupyter 笔记本中查看 3D 图像。您可以在那里看到图像的直方图。