A lattice Boltzmann method applied to the fluid image registration

Alahyane, Mohamed; Hakim, Abdelilah; Laghrib, Amine; Raghay, Said

doi:10.1016/j.amc.2018.12.051

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…In our work, we propose a new method to control these weight parameters based on Boltzmann function. Boltzmann function has shown efficiency with the Simulate Annealing algorithm [24][25][26][27][28][29][30] as an acceptance probability function which allows to avoid getting stuck at a local minimum. The temperature T gradually reduces so that the probability of accepting an up-hill move also gradually reduces.…”

Section: The Modified Choice Function With Boltzmann Functionmentioning

confidence: 99%

A modified choice function hyper-heuristic with Boltzmann function

Mellouli¹,

Hafidi²,

Abdelmoutalib³

2021

Math. Model. Comput.

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Hyper-heuristics are a subclass of high-level research methods that function in a low-level heuristic research space. Their aim objective is to improve the level of generality for solving combinatorial optimization problems using two main components: a methodology for the heuristic selection and a move acceptance criterion, to ensure intensification and diversification [1]. Thus, rather than working directly on the problem's solutions and selecting one of them to proceed to the next step at each stage, hyper-heuristics operates on a low-level heuristic research space. The choice function is one of the hyper-heuristics that have proven their efficiency in solving combinatorial optimization problems [2–4]. At each iteration, the selection of heuristics is dependent on a score calculated by combining three different measures to guarantee both intensification and diversification for the heuristics choice process. The heuristic with the highest score is therefore chosen to be applied to the problem. Therefore, the key to the success of the choice function is to choose the correct weight parameters of its three measures. In this study, we make a state of the art in hyper-heuristic research and propose a new method that automatically controls these weight parameters based on the Boltzmann function. The results obtained from its application on five problem domains are compared with those of the standard, modified choice function proposed by Drake et al. [2,3].

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Section: The Modified Choice Function With Boltzmann Functionmentioning

confidence: 99%

A modified choice function hyper-heuristic with Boltzmann function

Mellouli¹,

Hafidi²,

Abdelmoutalib³

2021

Math. Model. Comput.

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“…Generally, image registration problems can be classified into two types: intensity-based methods and feature-based methods. The first type methods are based on the image's gray spaces [2,3,20], while the second ones exploit the difference features existing between the given images, such as regions and edges, to find the correspondences in the image's feature spaces [23]. In most cases, the image registration task is formulated as an optimization problem involving a distance measure to evaluate the similarity.…”

Section: Introductionmentioning

confidence: 99%

An Improved Simplec Scheme for Fluid Registration

Alahyane

Hakim²,

Laghrib

et al. 2023

Mathematical Modelling and Analysis

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The image registration is always a strongly ill-posed problem, a stable numerical approach is then desired to better approximate the deformation vectors. This paper introduces an efficient numerical implementation of the Navier Stokes equation in the fluid image registration context. Although fluid registration approaches have succeeded in handling large image deformations, the numerical results are sometimes inconsistent and unexpected. This is related, in fact, to the used numerical scheme which does not take into consideration the different properties of the continuous operators. To take into account these properties, we use a robust numerical scheme based on finite volume with pressure correction. This scheme, which is called by the Semi-Implicit Method for Pressure-Linked Equation-Consistent (SIMPLEC), is known for its stability and consistency in fluid dynamics context. The experimental results demonstrate that the proposed method is more efficient and stable, visually and quantitatively, compared to some classical registration methods.

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“…Using the Chapman Enskog transformation, a partial differential equation can be obtained, including the level set function. This method's advantage is that implementing a computer program is relatively simple, and the execution time is shorter [17]. Balla-Arabé and Gao (2012) proposed image multi-thresholding by combining the LBM and a localized level set algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing

2021

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The rapid development of computer technology has had a significant influence on advances in medical science. This development concerns segmenting medical images that can be used to help doctors diagnose patient diseases. The boundary between objects contained in an image is captured using the level set function. The equation of the level set function is solved numerically by combining the Lattice Boltzmann (LBM) method and fuzzy clustering. Parallel processing using a graphical processing unit (GPU) accelerates the execution of the segmentation process. The results showed that image segmentation with a relatively large size could be done quickly. The use of parallel programming with the GPU can accelerate up to 39.22 times compared to the speed of serial programming with the CPU. In addition, the comparisons with other research and benchmark data show consistent results.

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A lattice Boltzmann method applied to the fluid image registration

Cited by 7 publications

References 46 publications

A modified choice function hyper-heuristic with Boltzmann function

A modified choice function hyper-heuristic with Boltzmann function

An Improved Simplec Scheme for Fluid Registration

Medical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing

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