Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques

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 i...

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Vydáno v:	SN computer science Ročník 4; číslo 5; s. 427
Hlavní autoři:	Kanadath, Anusree, Jothi, J. Angel Arul, Urolagin, Siddhaling
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.01.2023 Springer Nature B.V
Témata:	Algorithms Automation Breast cancer Colonoscopy Colorectal cancer Computer Imaging Computer Science Computer Systems Organization and Communication Networks COVID-19 Data Structures and Information Theory Datasets Heuristic methods Histopathology Image processing Image segmentation Information Systems and Communication Service Lesions Medical imaging Methods Multilevel Optimization algorithms Optimization techniques Original Research Particle swarm optimization Pattern Recognition and Graphics Performance evaluation Software Engineering/Programming and Operating Systems Swarm intelligence Thyroid gland Vision Image segmentation Histopathology Nature inspired algorithms Particle swarm optimization Thresholding
ISSN:	2661-8907, 2662-995X, 2661-8907
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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.
AbstractList	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. 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.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.
ArticleNumber	427
Author	Jothi, J. Angel Arul Kanadath, Anusree Urolagin, Siddhaling
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Snippet	Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology...
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SubjectTerms	Algorithms Automation Breast cancer Colonoscopy Colorectal cancer Computer Imaging Computer Science Computer Systems Organization and Communication Networks COVID-19 Data Structures and Information Theory Datasets Heuristic methods Histopathology Image processing Image segmentation Information Systems and Communication Service Lesions Medical imaging Methods Multilevel Optimization algorithms Optimization techniques Original Research Particle swarm optimization Pattern Recognition and Graphics Performance evaluation Software Engineering/Programming and Operating Systems Swarm intelligence Thyroid gland Vision
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Title	Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques
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