Chakraborty Sayan, Pradhan Ratika, S Ashour Amira, Moraru Luminita, Dey Nilanjan
Department of Computer Applications, SMIT, Sikkim Manipal University, Sikkim 737136, India.
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt.
Entropy (Basel). 2020 Jun 15;22(6):659. doi: 10.3390/e22060659.
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.
图像配准在医学成像中起着至关重要的作用。在这项工作中，提出了一种基于灰狼优化器（GWO）的非刚性恶魔配准方法来支持视网膜图像配准过程。对所提出的基于GWO的恶魔配准框架与布谷鸟搜索、萤火虫算法以及基于粒子群优化的恶魔配准进行了对比研究。此外，还对使用所提出的GWO优化的不同恶魔配准方法进行了对比分析，如王式恶魔、唐式恶魔和蒂里翁式恶魔。结果表明，基于GWO的框架具有优越性，其相关性达到0.9977，与使用其他优化算法相比处理速度更快。此外，基于GWO的王式恶魔比唐式恶魔和蒂里翁式恶魔框架具有更高的准确性。它还实现了最佳的较少配准误差，为8.36×10 。
