如何在python中计算3D图像的互信息

import numpy as np
from scipy import ndimage

image1 = np.load('image1.npy')  # 加载第一个3D图像数据
image2 = np.load('image2.npy')  # 加载第二个3D图像数据

histogram1, _ = np.histogram(image1.flatten(), bins=256, range=[0, 256])  # 计算第一个图像的直方图
histogram2, _ = np.histogram(image2.flatten(), bins=256, range=[0, 256])  # 计算第二个图像的直方图

normalized_histogram1 = histogram1 / float(np.sum(histogram1))  # 归一化第一个图像的直方图
normalized_histogram2 = histogram2 / float(np.sum(histogram2))  # 归一化第二个图像的直方图

joint_histogram, _, _ = np.histogram2d(image1.flatten(), image2.flatten(), bins=256, range=[[0, 256], [0, 256]])  # 计算联合直方图
joint_probability = joint_histogram / float(np.sum(joint_histogram))  # 计算联合概率
individual_probability = np.outer(normalized_histogram1, normalized_histogram2)  # 计算各自概率的外积
mutual_information = np.sum(joint_probability * np.log2(joint_probability / individual_probability))  # 计算互信息

print("互信息：", mutual_information)