计算三维图像的互信息量
计算三维图像的互信息量
提问于 2020-08-12 16:53:57
请问，如何使用Python计算两张三维图像的互信息值和标准化互信息值？任何帮助都将不胜感激。我曾尝试使用sklearn中的函数来计算互信息值a，但我发现在旋转或平移图像后再次计算互信息值时，得到的是值B。A和b之间的差异非常小，所以我个人认为这种方法不适用于图像。我说的对吗?这是我在sklearn包中使用的代码。
def NMI(img1,img2):
img1_ = sitk.GetArrayFromImage(img1)
img2_ = sitk.GetArrayFromImage(img2)
img2_ = np.reshape(img2_, -1)
img1_ = np.reshape(img1_, -1)
normalized_mutual_infor = mr.normalized_mutual_info_score(img1_, img2_)
nmi = normalized_mutual_infor
print(nmi)
fixed_image = sitk.ReadImage(r"D:\Lung CT\RIDER Lung CT\001_1.mha", sitk.sitkFloat32)
moving_image = sitk.ReadImage(r"D:\Lung CT\RIDER Lung CT\001_2.mha", sitk.sitkFloat32)
tfm1 = sitk.ReadTransform(r'D:\6freedom\1_text2.tfm')
x = tfm1.GetParameters()[3]
y = tfm1.GetParameters()[4]
z = tfm1.GetParameters()[5]
transform1 = sitk.Euler3DTransform(tfm1)
transform1.SetParameters((0, 0, 0, x, y, z))
resample = sitk.Resample(moving_image, fixed_image, transform1, sitk.sitkLinear, 0.0, moving_image.GetPixelID())
NMI(fixed_image, resample)
#When the parameter is (0,0,0, x, y, z), the result is 0.524628297588729
#When the parameter is (1,0,0, x, y, z), the result is 0.4657578384754303
#The unit of rotation is radians, so the image has been rotated a lot,
#but the difference between the two results is very small.回答 1
Stack Overflow用户
发布于 2020-08-26 05:25:10
从这个很好的notebook中，似乎可以使用输入图像的联合直方图，例如
import numpy as np
def mutual_information(hgram):
# Mutual information for joint histogram
# Convert bins counts to probability values
pxy = hgram / float(np.sum(hgram))
px = np.sum(pxy, axis=1) # marginal for x over y
py = np.sum(pxy, axis=0) # marginal for y over x
px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals
# Now we can do the calculation using the pxy, px_py 2D arrays
nzs = pxy > 0 # Only non-zero pxy values contribute to the sum
return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs]))
hist_2d, x_edges, y_edges = np.histogram2d(img1.ravel(),img2.ravel(),bins=20)
mi = mutual_information(hist_2d)
print(mi)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/63373115
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