How to segment blood vessels python opencv

问题

I am trying to segment the blood vessels in retinal images using Python and OpenCV. Here is the original image:

Ideally I want all the blood vessels to be very visible like this (different image):

Here is what I have tried so far. I took the green color channel of the image.

img = cv2.imread('images/HealthyEyeFundus.jpg')
b,g,r = cv2.split(img)

Then I tried to create a matched filter by following this article and this is what the output image is:

Then I tried doing max entropy thresholding:

def max_entropy(data):
    # calculate CDF (cumulative density function)
    cdf = data.astype(np.float).cumsum()

    # find histogram's nonzero area
    valid_idx = np.nonzero(data)[0]
    first_bin = valid_idx[0]
    last_bin = valid_idx[-1]

    # initialize search for maximum
    max_ent, threshold = 0, 0

    for it in range(first_bin, last_bin + 1):
        # Background (dark)
        hist_range = data[:it + 1]
        hist_range = hist_range[hist_range != 0] / cdf[it]  # normalize within selected range & remove all 0 elements
        tot_ent = -np.sum(hist_range * np.log(hist_range))  # background entropy

        # Foreground/Object (bright)
        hist_range = data[it + 1:]
        # normalize within selected range & remove all 0 elements
        hist_range = hist_range[hist_range != 0] / (cdf[last_bin] - cdf[it])
        tot_ent -= np.sum(hist_range * np.log(hist_range))  # accumulate object entropy

        # find max
        if tot_ent > max_ent:
            max_ent, threshold = tot_ent, it

    return threshold


img = skimage.io.imread('image.jpg')
# obtain histogram
hist = np.histogram(img, bins=256, range=(0, 256))[0]
# get threshold
th = max_entropy.max_entropy(hist)
print th

ret,th1 = cv2.threshold(img,th,255,cv2.THRESH_BINARY)

This is the result I'm getting, which is obviously not showing all the blood vessels:

I've also tried taking the matched filter version of the image and taking the magnitude of its sobel values.

img0 = cv2.imread('image.jpg',0)
sobelx = cv2.Sobel(img0,cv2.CV_64F,1,0,ksize=5)  # x
sobely = cv2.Sobel(img0,cv2.CV_64F,0,1,ksize=5)  # y
magnitude = np.sqrt(sobelx**2+sobely**2)

This makes the vessels pop out more:

Then I tried Otsu thresholding on it:

img0 = cv2.imread('image.jpg',0)
# # Otsu's thresholding
ret2,th2 = cv2.threshold(img0,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img0,(9,9),5)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

one = Image.fromarray(th2).show()
one = Image.fromarray(th3).show()

Otsu doesn't give adequate results. It ends up including noise in the results:

Any help is appreciated on how I can segment the blood vessels successfully.

回答1:

I worked on retina vessel detection for a bit few years ago, and there are different ways to do it:

If you don't need a top result but something fast, you can use oriented openings, see here and here.
Then you have an other version using mathematical morphology version here.

For better results, here are some ideas:

Personally, I used combination of Gabor filters, and results where pretty good. See the segmentation result here on the first image of drive.
And Gabor can be combined with learning for a good result, or here.
Few years ago, they claimed to have the best algorithm, but I've never had the opportunity to test it. I was sceptic about the performance gap and the way they thresholded the line detector results, it was kind of obscure.
But I know that nowadays, many people try to tackle the problem using CNN, but I've not heard about significant improvements.

来源：https://stackoverflow.com/questions/38403205/how-to-segment-blood-vessels-python-opencv

标签

python

image

OpenCV

computer-vision

edge-detection