Fine-Sliced Image Area Segmentation: A Detailed Explanation of OpenCV Image Segmentation Algorithms, from Threshold Segmentation to Deep Learning

# 1. Overview of Image Segmentation

Image segmentation is a vital technique in computer vision that decomposes an image into regions or objects with different characteristics. Depending on various principles and methods, image segmentation algorithms can be categorized into threshold-based segmentation, region-based segmentation, edge-based segmentation, clustering-based segmentation, and deep learning-based segmentation.

This chapter will first introduce the basic concepts and classifications of image segmentation, and then delve into a detailed explanation of threshold-based image segmentation. Threshold-based image segmentation is a simple yet effective segmentation method that divides image pixels into foreground and background by setting a threshold.

# 2. Threshold-based Image Segmentation

### 2.1 Fundamental Principles of Threshold Segmentation

Threshold segmentation is a straightforward image segmentation technique that categorizes image pixels into two classes: target pixels and background pixels. Target pixels are those greater than or equal to a given threshold, while background pixels are those less than the threshold.

#### 2.1.1 Global Threshold Segmentation

Global threshold segmentation uses a single threshold to segment the entire image. For each pixel, if the pixel value is greater than or equal to the threshold, it is labeled as a target pixel; otherwise, it is labeled as a background pixel.

import cv2

# Read the image
image = cv2.imread('image.jpg')

# Set the threshold
threshold = 128

# Perform global threshold segmentation
_, binary_image = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY)

# Display the segmentation result
cv2.imshow('Binary Image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

**Logical Analysis:**

* `cv2.threshold()` function performs global threshold segmentation.
* The `threshold` parameter specifies the threshold value.
* The `255` parameter specifies the intensity value of target pixels.
* The `cv2.THRESH_BINARY` parameter specifies the binary segmentation type.

#### 2.1.2 Local Threshold Segmentation

Local threshold segmentation uses different thresholds to segment different areas of the image. For each pixel, it calculates the average or median of its neighborhood and then uses that value as the threshold.

import cv2

# Read the image
image = cv2.imread('image.jpg')

# Set local threshold segmentation parameters
blockSize = 31
C = 15

# Perform local threshold segmentation
binary_image = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, blockSize, C)

# Display the segmentation result
cv2.imshow('Binary Image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

**Logical Analysis:**

* `cv2.adaptiveThreshold()` function performs local threshold segmentation.
* The `255` parameter specifies the intensity value of target pixels.
* The `cv2.ADAPTIVE_THRESH_MEAN_C` parameter specifies that the average is used as the threshold.
* The `cv2.THRESH_BINARY` parameter specifies the binary segmentation type.
* The `blockSize` parameter specifies the neighborhood size.
* The `C` parameter specifies a constant to adjust the threshold.

### 2.2 Algorithms and Applications of Threshold Segmentation

#### 2.2.1 Otsu Threshold Segmentation

Otsu threshold segmentation is an algorithm that automatically selects the threshold. It finds the optimal threshold by maximizing the between-class variance.

import cv2

# Read the image
image = cv2.imread('image.jpg')

# Perform Otsu threshold segmentation
_, binary_image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)

# Display the segmentation result
cv2.imshow('Binary Image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

**Logical Analysis:**

* `cv2.threshold()` function performs Otsu threshold segmentation.
* The `0` parameter specifies no threshold is used.
* The `255` parameter specifies the intensity value of target pixels.
* The `cv2.THRESH_OTSU` parameter specifies the Otsu threshold segmentation type.

#### 2.2.2 Binary Segmentation

Binary segmentation is a special type of threshold segmentation that converts the image into a binary image with only two pixel values (black and white).

import cv2

# Read the image
image = cv2.imread('image.jpg')

# Perform binary segmentation
_, binary_image = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY)

# Display the segmentation result
cv2.imshow('Binary Image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

**Logical Analysis:**

* `cv2.threshold()` function performs binary segmentation.
* The `128` parameter specifies the threshold.
* The `255` parameter specifies the intensity value of target pixels.
* The `cv2.THRESH_BINARY` parameter specifies the binary segmentation type.

# 3.1 Region Growing Algorithm

#### 3.1.1 Principles of the Region Growing Algorithm

The region growing algorithm is an image segmentation algorithm based on the principle of similarity. The algorithm starts from a seed point and gradually merges adjacent pixels with similar features into the same region until a stopping condition is met.

The principles of the region growing algorithm are as follows:

1. **Select Seed Points:** First, a seed point is chosen as the starting point for re