Threshold Segmentation Algorithm Based on Maximum Between-Class Variance Criterion

The text describes a threshold segmentation algorithm based on the maximum between-class variance criterion. This algorithm's core principle involves determining optimal thresholds through pixel classification, where the inter-class distance between resulting categories is maximized to achieve superior image segmentation results. Specifically, the algorithm calculates variances between classes under different threshold values and selects the threshold that yields maximum variance as the optimal segmentation point. This approach typically involves computing histogram distributions and implementing an iterative search through possible threshold values (commonly 0-255 for 8-bit images) to identify the threshold that maximizes the between-class variance metric σ²_B(t) = ω₀(t)ω₁(t)[μ₀(t)-μ₁(t)]², where ω represents class probabilities and μ denotes class means. Through this mathematical optimization, we obtain more precise and clearer image segmentation outcomes with reduced computational complexity compared to exhaustive search methods.