Immunohistochemical image segmentation with improved AGA-Otsu

Immunohistochemistry is an essential technique in the quantitative evaluation of tumors, significantly impacting the diagnosis, treatment and prognosis of esophageal cancer. This paper presents a novel method for segmenting immunohistochemical images of esophageal cancer cells, which improves upon the adaptive genetic algorithm and the Otsu algorithm to address issues of inconsistent staining and imprecise segmentation. The method introduces a newly designed threshold discriminant function that takes into account both within-class and between-class variances. Utilizing an enhanced adaptive genetic algorithm, the method efficiently determines the optimal threshold, while an upgraded crossover probability function prevents the algorithm from settling on local optima. The experimental results show that the algorithm can achieve a segmentation accuracy of 95.3%, surpassing the performance of the standard Otsu and genetic algorithms, especially in processing immunohistochemical images with uneven staining.