Accurately detect the semicircular region in the given grayscale image

I've an almost semicircular object in a grayscale image for which I intend to create a mask. I've tried some preprocessing + edge detections + filtering. however, there are still some irregularities .

Any suggestions on how to improve the mask?

If we assume that you have a half circle or (axis aligned) half ellipse, then one approach would be to make a copy of the image, flip it vertically and then vertically stack the two images. Then try HoughCircle (assuming it is close to a circle) in Python/OpenCV.

Input:

import cv2
import numpy as np

# read the image as grayscale
img = cv2.imread('disk_blob.png', cv2.IMREAD_GRAYSCALE)
hh, ww = img.shape[:2]

# remove 4 px bright border at top and left
img2 = img[4:hh, 4:ww]

# make copy, flip vertically and vstack
img3 = img2.copy()
img3 = np.flipud(img3)
img3 = np.vstack((img2,img3))

# apply hough circle
circles = cv2.HoughCircles(img3, cv2.HOUGH_GRADIENT, 1, minDist=ww, param1=150, param2=10, minRadius=ww//4, maxRadius=ww//2)

# draw circle on copy of img3 in red
result = img3.copy()
result = cv2.merge([result,result,result])

for circle in circles[0]:
    # draw the circle in the output image, then draw a rectangle
    # corresponding to the center of the circle
    (x,y,r) = circle
    print("xcent:",x,"ycent:",y,"radius:",r)
    x = int(x)
    y = int(y)
    r = int(r)
    cv2.circle(result, (x, y), r, (0, 0, 255), 2)

# save results
cv2.imwrite('disk_blob_circle.png', result)

# show results
cv2.imshow('img3', img3)
cv2.imshow('result', result)
cv2.waitKey(0)

Result:

Circle Information:

xcent: 687.5 ycent: 848.5 radius: 491.9