How to detect good features for rotationally aligning microscope images to a template

Question

I'm working on a project to automatically rotate microscope image stacks of a fluid experiment so that they are lined up with images of the CAD template for the microfluidic chip. I am using the OpenCV package in Python for image processing. Having the correct rotational orientation is necessary so that the images can be masked properly for analysis. Our chips have markers filled with fluorescent dye that are visible in every frame. The template and a sample image look like the following (the template can be scaled to arbitrary size, but the relevant region of the images is typically ~100x100 pixels or so):

I have not been able to rotationally align the image to the CAD template. Typically, the misalignment between the CAD template and the images is less than a few degrees, which is still sufficient to interfere with analysis, so I need to be able to measure the rotational difference even if it is relatively small.

Following examples online I am using the following procedure:

1. Scale up the image to approximately the same size as the template using cubic interpolation (~800 x 800)
2. Threshold both images using Otsu's method
3. Find keypoints and extract descriptors using a built-in method (I've tried ORB, AKAZE, and BRIEF).
4. Match descriptors using a brute-force matcher with Hamming distance.
5. Take the best matches and use them to compute a partial affine transformation matrix
6. Use that matrix to infer a rotational shift, warping the one image to the other as a check.

Here's a sample of my code (borrowed in part from here ):

import numpy as np
import cv2
import matplotlib.pyplot as plt

MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.5

def alignImages(im1, im2,returnpoints=False):
    # Detect ORB features and compute descriptors.
    size1 = int(0.1*(np.mean(np.shape(im1))))
    size2 = int(0.1*(np.mean(np.shape(im2))))
    orb1 = cv2.ORB_create(MAX_FEATURES,edgeThreshold=size1,patchSize=size1)
    orb2 = cv2.ORB_create(MAX_FEATURES,edgeThreshold=size2,patchSize=size2)
    keypoints1, descriptors1 = orb1.detectAndCompute(im1, None)
    keypoints2, descriptors2 = orb2.detectAndCompute(im2, None)

    matcher = cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)
    matches = matcher.match(descriptors1,descriptors2)
    # Sort matches by score
    matches.sort(key=lambda x: x.distance, reverse=False)

    # Remove not so good matches
    numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
    matches = matches[:numGoodMatches]

    # Draw top matches
    imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
    cv2.imwrite("matches.jpg", imMatches)

    # Extract location of good matches
    points1 = np.zeros((len(matches), 2), dtype=np.float32)
    points2 = np.zeros((len(matches), 2), dtype=np.float32)
    for i, match in enumerate(matches):
        points1[i, :] = keypoints1[match.queryIdx].pt
        points2[i, :] = keypoints2[match.trainIdx].pt
    # Find homography
    M, inliers = cv2.estimateAffinePartial2D(points1,points2)
    height, width = im2.shape
    im1Reg = cv2.warpAffine(im1,M,(width,height))
    return im1Reg, M

if __name__ == "__main__":
    test_template = cv2.cvtColor(cv2.imread("test_CAD_cropped.png"),cv2.COLOR_RGB2GRAY)
    test_image = cv2.cvtColor(cv2.imread("test_CAD_cropped.png"),cv2.COLOR_RGB2GRAY)
    fx = fy = 88/923
    test_image_big = cv2.resize(test_image,(0,0),fx=1/fx,fy=1/fy,interpolation=cv2.INTER_CUBIC)
    ret, imRef_t = cv2.threshold(test_template,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    ret, test_big_t = cv2.threshold(test_image_big,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    imReg, M = alignImages(test_big_t,imRef_t)
    fig, ax = plt.subplots(nrows=2,ncols=2,figsize=(8,8))
    ax[1,0].imshow(imReg)
    ax[1,0].set_title("Warped Image")
    ax[0,0].imshow(imRef_t)
    ax[0,0].set_title("Template")
    ax[0,1].imshow(test_big_t)
    ax[0,1].set_title("Thresholded Image")
    ax[1,1].imshow(imRef_t - imReg)
    ax[1,1].set_title("Diff")
    plt.show()

In this example, I get the following bad transformation because there are only 3 matching keypoints and they are all incorrect:

I find that regardless of my keypoint/descriptor parameters I tend to get too few "good" features. Is there anything I can do to pre-process my images better to get good features more reliably, or is there a better method to align my images to this template that doesn't involve keypoint matching? The specific application of this experiment means that I can't use the patented keypoint extractor/descriptors like SURF and SIFT.

Answer 1

A good method to align two images based on rotation, translation and scaling only is the Fourier Mellin transform .

Here is an example using the implementation in DIPlib (disclosure: I'm an author):

import PyDIP as dip

# load data
image = dip.ImageRead('image.png')
template = dip.ImageRead('template.png')
template = template.TensorElement(0) # this one is RGB, take any one channel

# pad the two images with zeros so they have equal sizes
sz = [max(image.Size(0), template.Size(0)), max(image.Size(1), template.Size(1))]
image = image.Pad(sz)
template = template.Pad(sz)

# match
res = dip.FourierMellinMatch2D(template, image)

# display
dip.JoinChannels((template,res,res)).Show()

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However, there are many other approaches. A key thing here is that both the template and the image are quite simple, and very similar. This makes registration very easy.

For example, assuming you have the proper scaling of the template (this should not be a problem I presume), all you need to do is find the rotation and the translation. You can brute-force the rotations, simply rotating the image over a set of small angles, and matching each of the results with the template (cross-correlation). The one with the best match (largest cross-correlation value) has the appropriate rotation. If you need to have a very precise rotation estimation, you can do a second set of angles close to the best choice in the first set.

Cross-correlation is cheap and easy to compute, and leads to high precision translation estimates (the Fourier Mellin method makes extensive use of it). Don't just find the pixel with the largest value in the cross-correlation output, you can fit a parabola to the few pixels around this one and use the location of the maximum of the fitted parabola. This leads to sub-pixel estimates of translation.