间质性肺病特征提取的Python代码

间质性肺病（ILD）的特征提取通常涉及医学图像处理和分析，常用Python库如OpenCV、Pandas、Numpy和Scikit-image等。这里是一个简化的例子，展示如何使用这些库对CT扫描图像进行预处理并提取某些特征：

import cv2
import numpy as np
import pandas as pd

# 加载和读取CT图像
def load_ct_image(path):
    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    return img

# 图像预处理，例如平滑和二值化
def preprocess(image):
    smoothed = cv2.GaussianBlur(image, (5, 5), 0)
    _, binary = cv2.threshold(smoothed, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    return binary

# 提取特征，比如基于形态学的操作（计数孔洞或分割区域）
def extract_features(binary_image):
    contours, _ = cv2.findContours(binary_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    features = {
        'number_of_contours': len(contours),
        'mean_contour_area': np.mean([cv2.contourArea(c) for c in contours]),
        # 更多特征可以添加，如最小外接矩形、形状复杂度等
    }
    return features

# 示例数据路径
image_path = 'path_to_your_CT_scan.png'

# 执行操作
original_img = load_ct_image(image_path)
processed_img = preprocess(original_img)
features = extract_features(processed_img)

# 将结果保存到DataFrame
df = pd.DataFrame(data=[features], columns=features.keys())
print(df)