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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Veröffentlicht in:SN computer science Jg. 4; H. 5; S. 427
Hauptverfasser: Kanadath, Anusree, Jothi, J. Angel Arul, Urolagin, Siddhaling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.01.2023
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01915-w