Dataset Information

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: Introduction.

ABSTRACT:

Background

There are various applications for medical image fusion schemes in different medical clinics. Here, the generalized version of the homomorphic filtering technique involving the Fourier domain for image and signal processing is a proper method.

Methods

The methods on the wavelet transform proposes some advantages in the discretization of multimodality medical images fusion, conducted in the Fourier spectrum. In the present study, an optimal version of the homomorphic fusion, namely optimum homomorphic wavelet fusion (OHWF) on the hybrid particle swarm and ant colony optimization methods, is presented. The presented OHWF, including some domains including wavelet and logarithmic, and besides, the wavelet allows the OHWF technique to decompose the images in the multi-level process.

Results

In this work, the modality one approximation coefficients and the coefficients belong to modality two are presented in adder1. While in the case of adder two, the modality one optimal scaled detailed constant values of modality one and the approximation coefficients refer to modality two are added in conjunction. The pixel-based averaged principle is applied to fuse the address one and two results simultaneously. First, the intended fusion technique is authenticated, applying different fusion assessment metrics for MR-PET, MR-SPECT, MR T1-T2, and MR-CT image fusions. And then, the proposed hybrid particle swarm optimizer (PSO) and ACO algorithms applied to obtain the best image fusion.

Conclusions

The empirical data illustrates that the presented method performs a desiring ability in image fusion in the case of functional and structural data.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC7729343 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: Introduction.

Jiang Yonghong Y Ma Yaning Y

Annals of translational medicine 20201101 22

<h4>Background</h4>There are various applications for medical image fusion schemes in different medical clinics. Here, the generalized version of the homomorphic filtering technique involving the Fourier domain for image and signal processing is a proper method.<h4>Methods</h4>The methods on the wavelet transform proposes some advantages in the discretization of multimodality medical images fusion, conducted in the Fourier spectrum. In the present study, an optimal version of the homomorphic fus ...[more]

PMID: 33313227

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Project description:This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants, namely biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature coupled with a cognitive bias mechanism that enables particles to make decisions on cooperation. The second algorithm, AHPSO, conceptualises particles in the swarm as energy-driven agents with bio-inspired altruistic behaviour, which allows for the formation of lending-borrowing relationships. The mechanisms underlying these algorithms provide new approaches to maintaining swarm diversity, which contributes to the prevention of premature convergence. The new algorithms were tested on the 30, 50 and 100-dimensional CEC'13, CEC'14 and CEC'17 test suites and various constrained real-world optimisation problems, as well as against 13 well-known PSO variants, the CEC competition winner, differential evolution algorithm L-SHADE and the recent bio-inspired I-CPA metaheuristic. The experimental results show that both the BEPSO and AHPSO algorithms provide very competitive performance on the unconstrained test suites and the constrained real-world problems. On the CEC13 test suite, across all dimensions, both BEPSO and AHPSO performed statistically significantly better than 10 of the 15 comparator algorithms, while none of the remaining 5 algorithms performed significantly better than either BEPSO or AHPSO. On the CEC17 test suite, on the 50D and 100D problems, both BEPSO and AHPSO performed statistically significantly better than 11 of the 15 comparator algorithms, while none of the remaining 4 algorithms performed significantly better than either BEPSO or AHPSO. On the constrained problem set, in terms of mean rank across 30 runs on all problems, BEPSO was first, and AHPSO was third.

Dataset Information

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: Introduction.

Background

Methods

Results

Conclusions

Publications

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: Introduction.

Similar Datasets

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