Hey there! Let's work through this background removal problem for your nucleus images—even with your current low-resolution, low-quality inputs, these strategies should work well, and they'll scale even better once you upgrade your image quality later. Since the approaches you've already tried (OpenCV's background subtraction, FFT magnitude subtraction) didn't deliver, here are some targeted alternatives tailored for microscopy-style imagery:

• Adaptive Local Thresholding
  Global thresholding often fails with uneven background lighting, which is common in microscopy shots. Instead, use adaptive thresholding that calculates thresholds for small local regions of the image. OpenCV's cv2.adaptiveThreshold() is perfect here—try using Gaussian-weighted neighborhood calculations to handle subtle brightness variations:

  import cv2
  
  gray_img = cv2.imread("your_image.png", cv2.IMREAD_GRAYSCALE)
  # Use inverse binary thresholding if nuclei are darker than background
  thresh = cv2.adaptiveThreshold(
      gray_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
      cv2.THRESH_BINARY_INV, 11, 2
  )
  

  Adjust the block size (11) and constant (2) based on your image's noise level and nucleus size.

• Otsu's Automatic Thresholding
  If your image has a bimodal histogram (distinct peaks for nuclei and background), Otsu's method will automatically find the optimal threshold value. It's fast and great for initial testing:

  ret, thresh = cv2.threshold(
      gray_img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU
  )
  

  The THRESH_BINARY_INV flag inverts the result if your nuclei are darker than the background—flip it to THRESH_BINARY if nuclei are brighter.

• Watershed Algorithm for Segmentation
  This is ideal if your nuclei are clustered or touching. Start with preprocessing to clean up noise, then define foreground/background markers to guide the segmentation:

  1. Apply a median blur to reduce noise: blurred = cv2.medianBlur(gray_img, 5)
  2. Use morphological opening to remove small background artifacts: opening = cv2.morphologyEx(blurred, cv2.MORPH_OPEN, np.ones((3,3), np.uint8))
  3. Mark sure background regions with dilation: sure_bg = cv2.dilate(opening, np.ones((3,3), np.uint8), iterations=3)
  4. Use distance transform to find sure foreground: dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
  5. Apply a threshold to get foreground markers: ret, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0)
  6. Run the watershed algorithm to separate nuclei from background.

• GrabCut for Interactive Foreground Extraction
  Even though GrabCut is often used for color images, you can adapt it for grayscale by duplicating the channel to make a 3-channel image. If you can roughly draw a bounding box around your nuclei, GrabCut will refine the segmentation automatically:

  import numpy as np
  import cv2
  
  # Convert grayscale to 3-channel
  img_color = cv2.cvtColor(gray_img, cv2.COLOR_GRAY2BGR)
  # Define a bounding box (x, y, width, height) around nuclei
  rect = (50, 50, 450, 290)
  # Initialize mask and temporary arrays
  mask = np.zeros(img_color.shape[:2], np.uint8)
  bgdModel = np.zeros((1,65), np.float64)
  fgdModel = np.zeros((1,65), np.float64)
  # Run GrabCut
  cv2.grabCut(img_color, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
  # Create final mask where foreground is marked
  final_mask = np.where((mask==2)|(mask==0), 0, 1).astype('uint8')
  # Apply mask to original image
  result = img_color * final_mask[:, :, np.newaxis]
  

• Preprocessing to Boost Results
  Before running any segmentation method, clean up your low-quality images to improve accuracy:

  • Denoise: Use cv2.medianBlur() (better for salt-and-pepper noise) or cv2.GaussianBlur() for general noise.
  • Contrast Enhancement: Try CLAHE (Contrast Limited Adaptive Histogram Equalization) to amplify subtle differences between nuclei and background without over-amplifying noise:

    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
    enhanced_img = clahe.apply(gray_img)
    

Start with Otsu's thresholding or adaptive thresholding first—they're quick to implement and will give you a baseline. Once you get higher-resolution images, the watershed algorithm or GrabCut will deliver even cleaner results.

内容的提问来源于stack exchange，提问作者Giulio