Optimization of Non-rigid Demons Registration Using Flower Pollination Algorithm

Chakraborty, Sayan; Pradhan, Ratika; Samanta, Sourav; Ashour, Amira S.

doi:10.1007/978-981-33-6104-1_2

Cited by 1 publication

(3 citation statements)

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“…From Table 4, it is clearly realized that after the 20th iteration, the convergence was achieved and the best fitness value obtained was of 0.9184. As previously noted, the best fitness was the correlation value [36] between the reference and resultant images. The original, reference and registered images are shown in Fig.…”

Section: Discussionmentioning

confidence: 85%

“…It can be observed in Table 4, k 1 , k 2 , and k 3 values for the flower pollination algorithm [36] based affine multimodal registration framework. The iterations were varied from 5 to 30 while keeping the generation set always at 15.…”

Section: Discussionmentioning

confidence: 98%

“…The pattern of pollination of flowers inspired the concept [36] behind flower pollination algorithm. The main aim of this bio-inspired algorithm is to find optimal solution for any problem statement.…”

Section: Flower Pollination Algorithmmentioning

confidence: 99%

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Effect of optimization framework on rigid and non-rigid multimodal image registration

Chakraborty¹,

Pradhan²,

Dey³

et al. 2022

ScienceAsia

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The process of transforming or aligning two images is known as image registration. In the present era, image registration is one of the most popular transformation tools in case of, for example, satellite as well as medical imaging analysis. Images captured by difference devices that can be processed under same registration model are called multimodal images. In this work, we present a multimodal image registration framework, upon which ant colony optimization (ACO) and flower pollination algorithms (FPA), which are two meta heuristics algorithms, are applied in order to improve the performance of a proposed rigid and non-rigid multimodal registration framework and decrease its processing time. The results of the ACO and FPA based framework were compared against particle swarm optimization and Genetic algorithm-based framework's results and seem to be promising.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 98%