提供能实现基于概率分布的超声图像与自然图像性质差异分析的完整代码，超声图像的文件路径是D:\zzz\na5，自然图像的文件路径是D:\zzz\us5,两种图像都有778张，请注意每张图像的长和宽不都一样可能需要调整，需要对比的特征有形态，纹理

这个任务可以使用图像处理库OpenCV和机器学习库scikit-learn来完成。具体实现步骤如下：

1. 首先，导入必要的库：

import cv2
import numpy as np
import os
from sklearn.metrics.pairwise import cosine_similarity

2. 读取图片并调整大小

def read_images(path, size=None):
    images = []
    for file_name in os.listdir(path):
        if file_name.endswith('.jpg'):
            file_path = os.path.join(path, file_name)
            img = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
            if size is not None:
                img = cv2.resize(img, size)
            images.append(img)
    return images

3. 提取特征：形态和纹理

def extract_features(images):
    shapes = []
    textures = []
    for img in images:
        # 形态特征
        _, contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        area = 0
        perimeter = 0
        for contour in contours:
            area += cv2.contourArea(contour)
            perimeter += cv2.arcLength(contour, True)
        shape_features = np.array([area, perimeter])

        # 纹理特征
        glcm = cv2.calcGLCM(img, [5], 0, 256, symmetric=True)
        texture_features = np.array([cv2.compareHist(cv2.normalize(cv2.calcHist([glcm], [0], None, [256], [0, 256]), None),
                                                     cv2.normalize(cv2.calcHist([glcm], [1], None, [256], [0, 256]), None),
                                                     cv2.HISTCMP_CORREL)])
        shapes.append(shape_features)
        textures.append(texture_features)
    return np.hstack((shapes, textures))

4. 计算两个特征向量之间的余弦相似度

def similarity(feature1, feature2):
    return cosine_similarity(feature1.reshape(1, -1), feature2.reshape(1, -1))[0][0]

5. 对比两组图像的特征

# 读取超声图像和自然图像
na_images = read_images('D:/zzz/na5')
us_images = read_images('D:/zzz/us5')

# 提取特征
na_features = extract_features(na_images)
us_features = extract_features(us_images)

# 计算相似度
similarities = []
for na_feature in na_features:
    row = []
    for us_feature in us_features:
        row.append(similarity(na_feature, us_feature))
    similarities.append(row)

6. 可视化结果

import matplotlib.pyplot as plt

plt.imshow(similarities, cmap='gray')
plt.show()

以上是一个简单的代码示例，其中可能需要针对自己的数据进行一些调整和优化。