能否提供一份用Python编写的，专为多区域（Multiple ROI）间质性肺病（ILD）特征提取设计的代码示例？

对于多区域间质性肺病（ILD）的特征提取，你需要先确定每个ROI（Region of Interest，感兴趣区），这通常涉及到图像分割。以下是一个简化版的Python代码，使用了`skimage`库进行分割和特征计算。请注意，实际应用中可能需要更复杂的算法，例如深度学习模型。

from skimage import measure, filters, io, draw
import numpy as np

# 读取CT图像
def load_ct_image(path, window_level=(400, 800)):
    img = io.imread(path, as_gray=True)
    img = (img - window_level[0]) / (window_level[1] - window_level[0])
    return img

# 多区域分割
def multi_roi_segmentation(img, regions_of_interest):
    labeled_image, num_labels = measure.label(img, background=0)
    segmentation_masks = []
    for region_id in regions_of_interest:
        mask = np.zeros_like(labeled_image)
        rr, cc = draw.circle_perimeter(region_id, labeled_image.shape)
        mask[rr, cc] = 1
        segmentation_masks.append(mask)
    return labeled_image, segmentation_masks

# 对每个ROI提取特征
def extract_features(roi_mask, img):
    filtered_img = filters.median(img, footprint=np.ones((3, 3)))
    roi_filtered_img = filtered_img * roi_mask
    mean_intensity = roi_filtered_img.mean()
    std_deviation = roi_filtered_img.std()
    # 添加更多特性，如纹理、边缘信息等
    return {'mean_intensity': mean_intensity, 'std_deviation': std_deviation}

# 使用示例
image_path = 'path_to_your_CT_scan.png'
regions_of_interest = [100, 200]  # 根据实际情况设置ROI坐标
img = load_ct_image(image_path)
labeled_image, masks = multi_roi_segmentation(img, regions_of_interest)
features_list = []

for i, mask in enumerate(masks):
    features = extract_features(mask, img)
    features['region'] = i
    features_list.append(features)

# 结果存储为DataFrame
results_df = pd.DataFrame(features_list)
print(results_df)