Improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation

Abstract

This paper presents an improved genetic algorithm focused on multi-threshold optimization for image segmentation in digital pathology. By innovatively enhancing the selection mechanism and crossover operation, the limitations of traditional genetic algorithms are effectively addressed, significantly improving both segmentation accuracy and computational efficiency. Experimental results demonstrate that the improved genetic algorithm achieves the best balance between precision and recall within the threshold range of 0.02 to 0.05, and it significantly outperforms traditional methods in terms of segmentation performance. Segmentation quality is quantified using metrics such as precision, recall, and F1 score, and statistical tests confirm the superior performance of the algorithm, especially in its global search capabilities for complex optimization problems. Although the algorithm's computation time is relatively long, its notable advantages in segmentation quality, particularly in handling high-precision segmentation tasks for complex images, are highly pronounced. The experiments also show that the algorithm exhibits strong robustness and stability, maintaining reliable performance under different initial conditions. Compared to general segmentation models, this algorithm demonstrates significant advantages in specialized tasks, such as pathology image segmentation, especially in resource-constrained environments. Therefore, this improved genetic algorithm offers an efficient and precise multi-threshold optimization solution for image segmentation, providing valuable reference for practical applications.

Keywords: Global search capability; Image segmentation; Improved genetic algorithm; Multi-threshold optimization; Robustness and stability.

Algorithm 1: Extracting Pixel Ratio Sequence of Epithelial and Stromal Regions

Fig. 1

Comparison of model performance on different datasets.

Fig. 2

Performance comparison between the improved GA and the original GA.

Fig. 3

Box plots comparing computation time and segmentation quality.

Fig. 4

Bar graphs displaying average computation time and segmentation quality.

Fig. 5

Performance changes and convergence speed during iteration.

Fig. 6

Comparison of final performance across.

Fig. 7

Comparison of performance fluctuations with different initializations and random seeds.

Fig. 8

The 3D plot of the time complexity analysis of the improved genetic algorithm.

Fig. 9

Performance comparison of algorithm variants.

Fig. 10

Total running time vs. number of generations.

Fig. 11

Rate of convergence of the genetic algorithm.

Fig. 12

Impact of mutation rates on convergence.

Fig. 13

Representative samples of original pathology images from the dataset.

Fig. 14

Segmentation results using the improved genetic algorithm.