Title: Bio-inspired optimisation algorithms in medical image segmentation: a review

Authors: Tian Zhang; Ping Zhou; Shenghan Zhang; Shi Cheng; Lianbo Ma; Huiyan Jiang; Yu-Dong Yao

Addresses: Software College, Northeastern University, Shenyang, China ' School of Electronic and Information Engineering, Beihang University, Beijing, China ' Software College, Northeastern University, Shenyang, China ' School of Computer Science, Shaanxi Normal University, Xi'an, China ' Software College, Northeastern University, Shenyang, China; Foshan Graduate School of Innovation, Northeastern University, Foshan, China ' Software College, Northeastern University, Shenyang, China ' Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA

Abstract: Medical image segmentation (MIS) is a primary task in medical image processing, with a great application prospect in medical image analysis and clinical diagnosis and treatment. However, MIS becomes a challenge due to the noisy imaging process of medical imaging devices and the complexity of medical images. Against this backdrop, the broad success of bio-inspired optimisation algorithms (BIOAs) has prompted the development of new MIS approaches leveraging BIOAs. As the first review of BIOAs for MIS applications, we present a comprehensive review of this recent literature, including genetic algorithm, particle swarm optimisation, ant colony optimisation, and artificial bee colony for blood vessel, organ, and tumour segmentation. We investigate the image modality and datasets that are used, discuss the application status of the four algorithms in MIS and address further research directions considering the advantages and disadvantages of each algorithm.

Keywords: bio-inspired optimisation; genetic algorithm; particle swarm optimisation; PSO; ant colony optimisation; ACO; artificial bee colony; ABC; medical image segmentation; MIS; bio-inspired optimisation algorithms; BIOAs.

DOI: 10.1504/IJBIC.2024.141449

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.2, pp.65 - 79

Received: 20 Mar 2023
Accepted: 06 Mar 2024
Published online: 13 Sep 2024 *

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