A comparative study of two meta-heuristic algorithms for MRI and CT images registration

Dida, Hedifa; Charif, Fella; Benchabane, Abderrazak

doi:10.1109/icoiact50329.2020.9332126

Cited by 3 publications

(2 citation statements)

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“…For the co-register of multi-modal images, we can find articles that use meta-heuristics to unravel the problem. For example, using meta-heuristics for the registration of magnetic resonance (MR) and computed tomography (CT) images, maximizing the MI, has proven to be an efficient tool [46]. In [46] the authors presented a comparison between the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) for registration of MR and CT images, maximizing the value of the MI.…”

Section: Registrationmentioning

confidence: 99%

“…For example, using meta-heuristics for the registration of magnetic resonance (MR) and computed tomography (CT) images, maximizing the MI, has proven to be an efficient tool [46]. In [46] the authors presented a comparison between the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) for registration of MR and CT images, maximizing the value of the MI. Their results demonstrate that GWO has a better accuracy and processing time than PSO.…”

Section: Registrationmentioning

confidence: 99%

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Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Hugo¹,

Aranda²,

Mejia-Zuluaga³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used.

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Section: Registrationmentioning

confidence: 99%

Section: Registrationmentioning

confidence: 99%