Image segmentation to find cells in biological images

Question

I have a bunch of images of cells and I want to extract where the cells are. I'm currently using circular Hough transforms and it works alright, but screws up regularly. Wondering if people have any pointers. Sorry this isn't a question specifically about software - it's how to get better performance in tis image segmentation problem.

I've tried other stuff in skimage with limited success, like the contour finding, edge detection and active contours. Nothing worked well out of the box, although it could just be that I didn't fiddle with the parameters correctly. I haven't done much image segmentation, and I don't really know how this stuff works or what the best ways are to jury-rig it.

Here is the code I currently am using that takes a grayscale image as a numpy array and looks for the cell as a circle:

import cv2
import numpy as np
smallest_dim = min(img.shape)
min_rad = int(img.shape[0]*0.05)
max_rad = int(img.shape[0]*0.5) #0.5
circles = cv2.HoughCircles((img*255).astype(np.uint8),cv2.HOUGH_GRADIENT,1,50,
    param1=50,param2=30,minRadius=min_rad,maxRadius=max_rad)
circles = np.uint16(np.around(circles))
x, y, r = circles[0,:][:1][0]

Here is an example where the code found the wrong circle as the boundary of the cell. It seems like it got confused by the gunk that is surrounding the cell:

Answer 1

I think one issue may be the plotting of circle (coordinates may be wrong).

Also, like @Nicos mentioned, there is alot of tweaking involved with traditional image processing to make specific cases work (while more recent machine learning approaches, the tweaking is so that models do not over-train), my attempt with skimage is displayed below. Radius range, number of circles, edge detection image, all needs to be tweaked... given the potential variation among and within images. Within this image, there are, at least to me, 3 circles with varying gradient, from the canny edge detection image, you can sort of see we are getting more than 3 circles, further, the "illumination" seems to vary at different locations (due to this being an sem image)?!

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

from skimage import data, color
from skimage.transform import hough_circle, hough_circle_peaks
from skimage.feature import canny
from skimage.draw import circle_perimeter
from skimage.util import img_as_ubyte

!wget https://i.sstatic.net/2tsWw.jpg

# rgb to gray https://stackoverflow.com/a/51571053/868736
im = imageio.imread('2tsWw.jpg')
gray = lambda rgb : np.dot(rgb[... , :3] , [0.299 , 0.587, 0.114]) 
gray = gray(im)  
image = np.array(gray[60:220,210:450])
plt.imshow(image,cmap='gray')

edges = canny(image, sigma=3,)
plt.imshow(edges,cmap='gray')

overlayimage = np.copy(image)

# https://scikit-image.org/docs/dev/auto_examples/edges/plot_circular_elliptical_hough_transform.html

hough_radii = np.arange(30, 60, 2)
hough_res = hough_circle(edges, hough_radii)

# Select the most prominent X circles
x=1
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,
                                           total_num_peaks=x)
# Draw them
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 4))
#image = color.gray2rgb(image)
for center_y, center_x, radius in zip(cy, cx, radii):
    circy, circx = circle_perimeter(center_y, center_x, radius)
    overlayimage[circy, circx] = 255

print(radii)
ax.imshow(overlayimage,cmap='gray')
plt.show()