基于OpenCV Python的模糊水平集视网膜液体分割

import cv2
import numpy as np

# 读取视网膜图像
image = cv2.imread('retina_image.png', cv2.IMREAD_GRAYSCALE)

# 预处理：高斯模糊去噪
blurred = cv2.GaussianBlur(image, (5, 5), 0)

# 应用Canny边缘检测
edges = cv2.Canny(blurred, 50, 150)

# 初始化水平集函数（例如，使用距离变换）
dist_transform = cv2.distanceTransform(edges, cv2.DIST_L2, 5)
_, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)

# 找到未知区域
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(edges, sure_fg)

# 标记连通区域
_, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1
markers[unknown == 255] = 0

# 应用分水岭算法进行分割
cv2.watershed(cv2.cvtColor(image, cv2.COLOR_GRAY2BGR), markers)

# 可视化结果
image[markers == -1] = [255, 0, 0]
cv2.imshow('Segmented Retina', image)
cv2.waitKey(0)
cv2.destroyAllWindows()