Three Image Segmentation Methods with Implementation Approaches

Image segmentation is a fundamental task in computer vision that aims to partition images into regions with similar characteristics. Below are three classic threshold-based segmentation methods with implementation details:

Global Threshold Otsu Segmentation Otsu's algorithm automatically determines the optimal segmentation threshold by maximizing inter-class variance, making it ideal for images with bimodal histograms. The core algorithm involves calculating the variance between foreground and background classes for all possible thresholds and selecting the threshold that maximizes this separation. In implementation, this typically requires computing a histogram of pixel intensities, iterating through possible threshold values, and evaluating the between-class variance using the formula: σ²(threshold) = ω₁(threshold)ω₂(threshold)[μ₁(threshold)-μ₂(threshold)]². This method provides efficient segmentation without manual intervention but has limitations with uneven illumination or multimodal distributions.

Iterative Global Threshold Segmentation This method achieves segmentation through progressive approximation of the optimal threshold: initialize a threshold value, segment the image into foreground and background based on the current threshold, calculate the mean intensity of both regions, then update the threshold to the average of these two means. The iterative process continues until the threshold converges (changes below a predefined tolerance). Code implementation typically involves a while loop that checks for convergence, with each iteration requiring mean calculations of the segmented regions. While this approach offers good adaptability, improper initial threshold selection may increase iteration cycles.

Local Threshold Segmentation For images with uneven illumination, local thresholding divides the image into sub-regions and calculates individual thresholds for each block (using methods like Otsu or mean-based approaches). Implementation often involves sliding windows or adaptive grid processing, where each window processes local features independently. The algorithm requires careful balancing of window size parameter - larger windows improve computational efficiency but may lose detail, while smaller windows preserve details but increase computation load. This method significantly enhances segmentation performance in complex environments but requires more computational resources.

Each method has specific application scenarios, and practical implementations often require selecting or combining multiple algorithms based on image characteristics and performance requirements.