Python: Calculating the angle between two bones in an x-ray

Question

I am trying to write a script to calculate the angle between two bones given an x-ray.

A sample x-ray would look like the following:

I am trying to calculate the midline of each bone, essentially a line following the midpoints of the two sides of a bone, and then compare the angle between the two midlines.

I have tried using OpenCV to get the outline of the bones, but it does not seem accurate enough and gets lots of extra data. I am stuck on how to move next and how I would calculate the midline. I am quite new to image processing but have experience with Python.

Getting edges using OpenCV results:

Code for OpenCV:

import cv2

# Load the image
img = cv2.imread("xray-3.jpg")

# Find the contours
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(img,60,200)
im2, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

hierarchy = hierarchy[0] # get the actual inner list of hierarchy descriptions

# For each contour, find the bounding rectangle and draw it
cv2.drawContours(img, contours, -1, (0,255,0), 3)

# Finally show the image
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

If it's not cheating, i'd recommend cropping the image to not in include as much of the labels and scales as possible without removing any areas of interest.

That being said, I think your method of getting the contours will be usable if you do some preprocessing to the image. One algorithm that might do the trick is a Difference of Gaussians (DoG) filter which will bring out the edges a little more. I modified slightly this code which will compute the DoG filter using a few different sigma and k values.

from skimage import io, feature, color, filters, img_as_float
from matplotlib import pyplot as plt

raw_img = io.imread('xray-3.jpg')
original_image = img_as_float(raw_img)
img = color.rgb2gray(original_image)

k = 1.6

plt.subplot(2,3,1)
plt.imshow(original_image)
plt.title('Original Image')

for idx,sigma in enumerate([4.0, 8.0, 16.0, 32.0]):
    s1 = filters.gaussian(img, k*sigma)
    s2 = filters.gaussian(img, sigma)

    # multiply by sigma to get scale invariance
    dog = s1 - s2
    plt.subplot(2,3,idx+2)
    print("min: {} max: {}".format(dog.min(), dog.max())
    plt.imshow(dog, cmap='RdBu')
    plt.title('DoG with sigma=' + str(sigma) + ', k=' + str(k))

ax = plt.subplot(2, 3, 6)
blobs_dog = [(x[0], x[1], x[2]) for x in feature.blob_dog(img, min_sigma=4, max_sigma=32, threshold=0.5, overlap=1.0)]
# skimage has a bug in my version where only maxima were returned by the above
blobs_dog += [(x[0], x[1], x[2]) for x in feature.blob_dog(-img,  min_sigma=4, max_sigma=32, threshold=0.5, overlap=1.0)]

#remove duplicates
blobs_dog = set(blobs_dog)

img_blobs = color.gray2rgb(img)
for blob in blobs_dog:
    y, x, r = blob
    c = plt.Circle((x, y), r, color='red', linewidth=2, fill=False)
    ax.add_patch(c)
plt.imshow(img_blobs)
plt.title('Detected DoG Maxima')

plt.show()

At first glance, it appears that sigma=8.0, k=1.6 might be your best bet as this seems to best exaggerate the edges of the lower leg while getting rid of the noise across it. Particularly over that of the subjects left (image right) leg. Give your edge detection another go and play around with k and sigma and let me know what you get :)

If the results look good you should be able to get a center point between the edges detected for either leg in each row of the image. Then just find the line of best fit for the mid points for either leg and you should be good to go. You will also need to isolate one leg from another, so again, if it's not cheating, maybe crop the image down the middle into two images.