Python 视网膜图像中的血管分割

我试图在视网膜图像中追踪血管。目前，我正在使用cv2的阈值功能，使血管与周围视网膜形成更多对比：

这就是结果：

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所有3种方法仍然有大量来自视网膜其他部分的背景噪声。如何提高血管跟踪的准确性？

这里有另一种方法，使用分割标准化，然后对轮廓区域进行过滤，来设置图像阈值

• 读取输入
• 变灰
• 应用形态学放大（或应用高斯模糊）
• 将输入除以放大的结果
• 门槛
• 倒置，使血管在黑色背景上为白色
• 找到所有轮廓
• 过滤轮廓以丢弃过小的轮廓
• 在黑色图像上以白色绘制剩余轮廓作为遮罩
• 将遮罩应用于阈值图像作为第一个结果
• 将掩码作为第二个结果应用于输入
• 保存结果

输入

分区图像：

阈值图像：

遮罩图像：

结果1：

结果2：

from matplotlib import pyplot as plt
import cv2

img = cv2.imread('misc images/eye.jpeg',0)
img = cv2.medianBlur(img,5)

ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
            cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            cv2.THRESH_BINARY,11,2)

titles = ['Original Image', 'Global Thresholding (v = 127)',
            'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [img, th1, th2, th3]

for i in range(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

import cv2
import numpy as np

# read the image
img = cv2.imread('retina_eye.jpg')

# convert to gray
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# apply morphology
kernel = cv2.getStructuringElement(cv2.MORPH_RECT , (5,5))
morph = cv2.morphologyEx(gray, cv2.MORPH_DILATE, kernel)

# divide gray by morphology image
division = cv2.divide(gray, morph, scale=255)

# threshold
thresh = cv2.threshold(division, 0, 255, cv2.THRESH_OTSU )[1] 

# invert
thresh = 255 - thresh

# find contours and discard contours with small areas
mask = np.zeros_like(thresh)
contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

area_thresh = 10000
for cntr in contours:
    area = cv2.contourArea(cntr)
    if area > area_thresh:
        cv2.drawContours(mask, [cntr], -1, 255, 2)

# apply mask to thresh
result1 = cv2.bitwise_and(thresh, mask)
mask = cv2.merge([mask,mask,mask])
result2 = cv2.bitwise_and(img, mask)

# save results
cv2.imwrite('retina_eye_division.jpg',division)
cv2.imwrite('retina_eye_thresh.jpg',thresh)
cv2.imwrite('retina_eye_mask.jpg',mask)
cv2.imwrite('retina_eye_result1.jpg',result1)
cv2.imwrite('retina_eye_result2.jpg',result2)

# show results
cv2.imshow('morph', morph)  
cv2.imshow('division', division)  
cv2.imshow('thresh', thresh)  
cv2.imshow('mask', mask)  
cv2.imshow('result1', result1)  
cv2.imshow('result2', result2)  
cv2.waitKey(0)
cv2.destroyAllWindows()