opencv-bioimage-analysis

# 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)

```bash
# 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

```python
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.

```python
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.

```python
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.

```python
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.

```python
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,