用于多模态生物医学图像配准的基于正态振动分布搜索的差分进化算法。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Peng Gui, Fazhi He, Bingo Wing-Kuen Ling, Dengyi Zhang, Zongyuan Ge
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引用次数: 0

摘要

在线性配准中,在执行一系列线性度量变换之后,浮动图像与参考图像在空间上对齐。此外,线性配准主要被认为是非刚性配准的预处理版本。为了更好地完成在基于成对强度的医学图像配准中寻找最优变换的任务,在这项工作中,我们提出了一种优化算法,称为基于正态振动分布搜索的差分进化算法(NVSA),该算法是对基于Bernstein搜索的差进化算法(BSD)的改进。我们重新设计了BSD算法的搜索模式,并导入了几个控制参数作为微调过程的一部分,以降低算法的难度。在本研究中,23个经典优化函数和16个真实世界的患者(产生41个多模式注册场景)被用于实验,以统计研究NVSA的问题解决能力。在所进行的实验中使用了九种元启发式算法。与常用的配准方法(如ANTS、Elastix和FSL)相比,我们的方法在RIRE数据集上实现了更好的配准性能。此外,我们证明,在不同的评估指标方面,无论是否进行初始空间转换,我们的方法都能表现良好,证明了其对各种临床需求和应用的通用性和稳健性。这项研究确立了这样一种观点,即基于元启发式的方法比常用的方法可以更好地完成线性配准任务;所提出的方法表明,作为一种预处理方法,它可以解决非刚性注册过程中遇到的实际临床和服务问题。NVSA的源代码可在https://github.com/PengGui-N/NVSA.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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