Artificial Computation in Biology and Medicine

Vicente, José Manuel Ferrández; Sánchez, José Luis Guzmán; López, Félix de la Paz; Toledo-Moreo, F. Javier; Adeli, Hojjat

doi:10.1007/978-3-319-18914-7

Cited by 10 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arce-Santana et al [13] made a new approach towards multimodal image registration when they used expectation-maximization (EM) to calculate displacement vectors that are used in non-rigid image registration algorithm to form the transformation matrix. In 2015, Vicente et al [2] discussed about a 3D registration technique which involved multimodal images of anatomic (MRI) and functional (fMRI and PET) brain data. They registered MRI, fMRI and PET brain images by diffusion registration to evaluate the similarity measurement among the target image and resultant image.…”

Section: Previous Workmentioning

confidence: 99%

“…In the wide domain of image processing and analysis, which includes, among others, feature extraction, pattern matching and image classification tasks, image registration has gained particular attention [1,2]. Recent trends in in this domain suggest that image registration is a major tool in the fields of medical imaging and satellite imaging.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chakraborty¹,

Pradhan²,

Dey³

et al. 2022

ScienceAsia

View full text Add to dashboard Cite

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.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%