OpenCV — Bio-image Computer Vision

Overview

OpenCV (cv2) provides optimized C++-backed image processing routines for preprocessing, segmentation, feature extraction, and video analysis of biological images. In life sciences, OpenCV is used for fluorescence image enhancement (background subtraction, CLAHE), morphological segmentation (watershed, contour detection), brightfield cell detection, and real-time microscopy stream processing. Unlike scikit-image (which emphasizes scientific measurement), OpenCV prioritizes computational speed and video support — making it ideal for preprocessing pipelines and real-time imaging applications.

When to Use

• Preprocessing fluorescence or brightfield images: background subtraction, CLAHE, Gaussian/median blur
• Detecting cell contours, blobs, or edges without deep learning (classical methods)
• Processing video streams from live-cell imaging microscopes in real-time
• Template matching for finding repeated structures (organelles, crystals, patterns)
• Applying morphological operations (erosion, dilation, opening, closing) for mask refinement
• Computing optical flow between video frames for cell tracking
• Use scikit-image instead for scientific morphometry, regionprops, and scientific image I/O (TIFF metadata)
• Use Cellpose or StarDist instead for deep-learning cell segmentation on fluorescence images

Prerequisites

• Python packages: opencv-python, numpy, matplotlib
• Optional: opencv-contrib-python for extra modules (SIFT, SURF, optical flow)

# Install OpenCV
pip install opencv-python

# Install with extra contributed modules (SIFT, SURF, etc.)
pip install opencv-contrib-python

# Verify
python -c "import cv2; print(cv2.__version__)"
# 4.10.0

Quick Start

import cv2
import numpy as np

# Read and display image info
img = cv2.imread("cells.tif", cv2.IMREAD_GRAYSCALE)
print(f"Shape: {img.shape}, dtype: {img.dtype}")
print(f"Min: {img.min()}, Max: {img.max()}")

# Apply Gaussian blur and threshold
blurred = cv2.GaussianBlur(img, (5, 5), 0)
_, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
print(f"Cells detected (rough): {np.sum(binary > 0)} foreground pixels")

Core API

Module 1: Image I/O and Color Space Conversion

Read, write, and convert images between color spaces.

import cv2
import numpy as np

# Read image (GRAYSCALE, COLOR, or UNCHANGED for 16-bit)
img_gray  = cv2.imread("cells.tif", cv2.IMREAD_GRAYSCALE)   # uint8
img_color = cv2.imread("rgb.tif", cv2.IMREAD_COLOR)         # BGR order!
img_16bit = cv2.imread("16bit.tif", cv2.IMREAD_UNCHANGED)   # uint16

print(f"Grayscale shape: {img_gray.shape}, dtype: {img_gray.dtype}")
print(f"Color shape:     {img_color.shape}")

# Color space conversions
img_rgb  = cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB)   # BGR → RGB
img_hsv  = cv2.cvtColor(img_color, cv2.COLOR_BGR2HSV)   # BGR → HSV
img_gray2 = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY) # BGR → gray

# Write image
cv2.imwrite("output.png", img_gray)
cv2.imwrite("output_16bit.tif", img_16bit)
print("Images written.")

Module 2: Filtering and Enhancement

Apply filters and contrast enhancement for image preprocessing.

import cv2
import numpy as np

img = cv2.imread("cells.tif", cv2.IMREAD_GRAYSCALE)

# Gaussian blur (noise reduction)
blurred = cv2.GaussianBlur(img, (7, 7), sigmaX=1.5)

# Median blur (salt-and-pepper noise)
median = cv2.medianBlur(img, 5)

# CLAHE: Contrast Limited Adaptive Histogram Equalization (for microscopy)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
clahe_img = clahe.apply(img)

# Top-hat filter for bright spots on dark background
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)

print(f"CLAHE range: [{clahe_img.min()}, {clahe_img.max()}]")
cv2.imwrite("clahe_enhanced.tif", clahe_img)

Module 3: Thresholding and Binary Segmentation

Convert grayscale images to binary masks using various thresholding methods.

import cv2
import numpy as np

img = cv2.imread("nuclei.tif", cv2.IMREAD_GRAYSCALE)

# Otsu's thresholding (automatic threshold selection)
thresh_val, otsu_mask = cv2.threshold(img, 0, 255,
                                      cv2.THRESH_BINARY + cv2.THRESH_OTSU)
print(f"Otsu threshold: {thresh_val:.0f}")

# Adaptive thresholding (handles uneven illumination)
adaptive = cv2.adaptiveThreshold(
    img, 255,
    cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
    cv2.THRESH_BINARY,
    blockSize=11,     # neighborhood size (odd)
    C=2,              # constant subtracted from mean
)

# For 16-bit images: normalize first
img_16 = cv2.imread("16bit_nuclei.tif", cv2.IMREAD_UNCHANGED)
img_8 = cv2.normalize(img_16, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)
_, mask_16 = cv2.threshold(img_8, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

print(f"Otsu mask foreground: {mask_16.sum() / 255} pixels")

Module 4: Contour Detection and Measurement

Find and measure cell contours from binary masks.

import cv2
import numpy as np
import pandas as pd

img = cv2.imread("cells.tif", cv2.IMREAD_GRAYSCALE)
blurred = cv2.GaussianBlur(img, (5, 5), 0)
_, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# Remove small objects with morphological opening
kernel = np.ones((3, 3), np.uint8)
cleaned = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=2)

# Find contours
contours, hierarchy = cv2.findContours(cleaned, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print(f"Objects detected: {len(contours)}")

# Measure each contour
records = []
for i, cnt in enumerate(contours):
    area = cv2.contourArea(cnt)
    if area < 50: continue  # skip tiny objects
    perimeter = cv2.arcLength(cnt, True)
    x, y, w, h = cv2.boundingRect(cnt)
    (cx, cy), radius = cv2.minEnclosingCircle(cnt)
    records.append({"cell_id": i, "area": area, "perimeter": perimeter,
                     "x": x, "y": y, "w": w, "h": h, "radius": radius})

df = pd.DataFrame(records)
print(f"Cells > 50 px²: {len(df)}")
print(df[["area", "perimeter", "radius"]].describe())

Module 5: Morphological Operations for Mask Refinement

Refine segmentation masks with morphological operations.

import cv2
import numpy as np

# Load binary mask (from thresholding or Cellpose)
mask = cv2.imread("rough_mask.png", cv2.IMREAD_GRAYSCALE)
_, mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)

# Structural elements
ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
rect    = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))

# Opening: remove small bright noise
opened = cv2.morphologyEx(mask, cv2.MORPH_OPEN, ellipse, iterations=1)

# Closing: fill small holes inside cells
closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, ellipse, iterations=2)

# Dilation: expand cell boundaries slightly
dilated = cv2.dilate(closed, ellipse, iterations=1)

# Distance transform for watershed seed generation
dist = cv2.distanceTransform(closed, cv2.DIST_L2, 5)
_, seeds = cv2.threshold(dist, 0.5 * dist.max(), 255, 0)
seeds = seeds.astype(np.uint8)
print(f"Potential cell centers: {cv2.connectedComponents(seeds)[0] - 1}")

Module 6: Video Processing for Live-Cell Imaging

Process video streams from time-lapse microscopy.

import cv2
import numpy as np

# Process a time-lapse video file
cap = cv2.VideoCapture("timelapse.avi")
fps = cap.get(cv2.CAP_PROP_FPS)
n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print(f"Video: {n_frames} frames at {fps} FPS")

# Background subtraction (remove static background)
bg_subtractor = cv2.createBackgroundSubtractorMOG2(
    history=50, varThreshold=25, detectShadows=False
)

frame_counts = []
frame_idx = 0
while cap.isOpened():
    ret, frame = cap.read()
    if not ret: break
    
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    fg_mask = bg_subtractor.apply(gray)
    
    # Count moving objects in