United equilibrium optimizer for solving multimodal image registration

Gui, Peng; He, Fazhi; Ling, Bingo Wing‐Kuen; Zhang, Dengyi

doi:10.1016/j.knosys.2021.107552

Cited by 17 publications

(5 citation statements)

References 42 publications

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“…In this section, six metaheuristic algorithms, including the proposed NVSA and the original BSD algorithm [ 22 ], the high-performance DA [ 43 ] and GWO [ 44 ] algorithms, and the SOTA BBOEL [ 45 ] and UEO [ 47 ] algorithms, are considered as the optimization methods for finding the best similarity metric between the moving and fixed images. To match the previous work, we refer to the parameter settings in [ 42 , 45 ], which include a population size of 30, a maximum number of generations of 100, a rotation angle in the range of [0, 360] and a translation in the range of [−30,30].…”

Section: Multimodal Medical Image Registration Resultsmentioning

confidence: 99%

“…Differential Evolution With Optional External Archive(JADE) 2. Differential Search Algorithm (DSA) Artificial registration scenarios [ 47 ] UEO CT/MRI Rigid CCRE Brain No 1. Biogeography-Based Optimization(BBO) 2.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, with the development of multimodal medical image registration, various methods that use metaheuristic algorithms for optimization purposes have been proposed. As shown in Table 1 , optimization algorithms including the coral reef optimization algorithm with substrate layers (CRO-SL) [ 42 ], the dragonfly algorithm (DA) [ 43 ], the gray wolf optimizer (GWO) [ 44 ], biography-based optimization with elite learning (BBOEL), the hybrid differential search algorithm (HDSA) [ 46 ] and the united equilibrium optimizer (UEO) [ 47 ] are applied to intensity-based medical image registration. These algorithms have demonstrated their superiority over many of the other algorithms listed above.…”

Section: Related Workmentioning

confidence: 99%

“…However, these methods have certain limitations that restrict their applicability to general multimodal brain tasks. For example, some of these algorithms are based on rigid transformations [ 44 – 47 ] and do not consider affine transformations, which are indispensable in practical applications. In addition, some algorithms are time-consuming [ 43 , 45 ] and have not been compared with classic registration tools [ 43 – 45 , 45 – 47 ], which limits their practicality.…”

Section: Related Workmentioning

confidence: 99%

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Normal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration

Gui

Ling

et al. 2023

Neural Comput & Applic

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In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA.

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Section: Multimodal Medical Image Registration Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Normal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration

Gui

Ling

et al. 2023

Neural Comput & Applic

Self Cite

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“…Additionally, the experimental findings unmistakably show that the suggested IEO outperforms m-EO, AEO, OB-L-EO, and HEO for the optimal power flow problem. A new version of EO known as the United Equilibrium Optimizer (UEO), which uses combined parameters in the equilibrium pool (EOU) and dynamic parameters (EOD) to provide balance in standard EO, was proposed by Gui et al in the year 2021 [134]. Furthermore, it is abundantly obvious from the experimental results that for multimodal image registration, the suggested UEO performs better than PSO, GWO, GA, GSA, SSA, and CMA-ES.…”

Section: Dynamic Equilibrium Optimizermentioning

confidence: 99%

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rai

Dhal

2023

Arch Computat Methods Eng

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There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.

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