Grey-Wolf-Based Wang's Demons for Retinal Image Registration.

Grey-Wolf-Based Wang's Demons for Retinal Image Registration.
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DOI:
10.3390/e22060659
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发表时间:
2020-06-15
期刊:
Entropy (Basel, Switzerland)
影响因子:
--
通讯作者:
Dey N
Dey N
中科院分区:
其他
文献类型:
--
作者:
Chakraborty S;Pradhan R;S Ashour A;Moraru L;Dey N

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图像配准在医学成像中具有重要的作用。在这项工作中,提出了一种基于灰狼优化器(GWO)的非刚性恶魔配准支持视网膜图像配准过程。提出的GWO为基础的恶魔注册框架与布谷鸟搜索,萤火虫算法,粒子群优化为基础的恶魔注册进行了比较研究。此外,不同的恶魔注册方法,如王的恶魔,唐的恶魔,和Thirion的恶魔,使用建议的GWO优化进行了比较分析。结果表明,与使用其他优化算法相比,基于GWO的框架具有0.9977的相关性和快速处理的优越性。此外,基于GWO的Wang的恶魔与Tang的恶魔和Thirion的恶魔框架相比,表现出更好的准确性。它还实现了最佳的8.36 × 10−5的较小配准误差。
Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5.
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