Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques

Abstract

Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu’s discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.

Improved Otsu Theory of Image Multi-threshold Segmentation by Incorporating Ant Colony Algorithm

“…In OTSU-ACO identification tasks, numerous statistics are utilized to measure a model's prediction capabilities, including Accuracy, f-measure, Sensitivity, Specificity, and Computational time. Particle swarm optimization (PSO) [20], Darwinian particle swarm optimization (DPSO) [20], and fractional order Darwinian particle swarm optimization (FODPSO) [20], One-dimensional OTSU [21], Two-dimensional OTSU [21], 2D OTSU-GA [21] were compared with our proposed method.…”

“…Notwithstanding these drawbacks, they show encouraging improvements in efficiency and accuracy, which are essential for accurate segmentation and diagnosis. Existing techniques such as (PSO) [20], (DPSO) [20], and (FODPSO) [20] have the following drawbacks: they are inefficient in handling large optimization landscapes, sensitive to parameter choices, and converge to local optima. These restrictions may result in less-than-ideal segmentation outcomes and higher computing expenses.…”