Efficient compression of volumetric medical images using Legendre moments and differential evolution

Hosny, Khalid M.; Khalid, Asmaa M.; Mohamed, Ehab R.

doi:10.1007/s00500-019-03922-7

Cited by 29 publications

(16 citation statements)

References 25 publications

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“…There are several signal compression techniques in the literature [ 19 , 22 , 26 , 33 ]. In order to show the efficiency of the proposed algorithm, a comparison with these techniques is made in terms of PRD% and CR%.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…COMs are defined from continuous orthogonal polynomials such as Zernike [ 36 ], Legendre [ 17 ], Fourier-Mellin [ 25 ], pseudo-Zernike [ 4 ] and Gaussian-Hermite [ 18 ] polynomials. The use of this type of moments is limited by two types of errors: the errors of discretization of the continuous space of the polynomials towards the discrete space of the image and the errors of approximation of the continuous integrals by discrete sums.…”

Section: Introductionmentioning

confidence: 99%

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Optimal reconstruction and compression of signals and images by Hahn moments and artificial bee Colony (ABC) algorithm

Bencherqui

Daoui

Karmouni

et al. 2022

Multimed Tools Appl

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In this paper, we present an efficient and optimal method for optimization of Hahn parameters a and b using the Artificial Bee Colony algorithm (ABC) in order to improve the quality of reconstruction and the compression of bio-signals and 2D / 3D color images of large sizes. The proposed methods are essentially based on two concepts: the development of a recursive calculation of the initial terms of Hahn polynomials in order to avoid the problems of instability of polynomial values and the use of ABC algorithm to optimize the values of the parameters a and b of the discrete orthogonal Hahn polynomials ( HPs ) during the reconstruction and the compression of bio-signals and 2D / 3D color images. The simulation results performed on bio-signals and on large size 2D / 3D color images clearly show the efficiency and superiority of the proposed methods over conventional methods in terms of reconstruction of signals and images.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal reconstruction and compression of signals and images by Hahn moments and artificial bee Colony (ABC) algorithm

Bencherqui

Daoui

Karmouni

et al. 2022

Multimed Tools Appl

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“…Step 1: Compute the coefficients of stable HPs using Eqs. ( 6) and (7) in the specific intervals defined by n ∈ 0, N 2 − 1 ; x ∈ 0, N 2 − 1 where x and n incrementing by 1…”

Section: Recurrence Relation With Respect To the Variable Xmentioning

confidence: 99%

“…This problem led scientists to develop 3D image analysis and processing methods, including the theory of moments that will be the subject of study in this article. In theory, the moment approach is divided into three main categories: The non-orthogonal moments, such as geometric and complex moments [5,6], the continuous orthogonal moments [7][8][9][10][11][12][13], and the discrete orthogonal moments (DOMs). We are going to focus mainly on DOMs such as the moments of Tchebichef [14,15], Krawtchouk [14][15][16][17], Hahn [18], Charlier [19][20][21], and last but not least Meixner [21,22], as these have concrete advantages over 3D image analysis.…”

mentioning

confidence: 99%

Partial 3D Image Reconstruction by Cuboids Using Stable Computation of Hahn Polynomials

Tahiri

Karmouni

Tahiri

et al. 2021

Lecture Notes in Electrical Engineering

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In this paper, we will present a new method of partial reconstruction of the 3D image. The latter is based on the following two concepts: one is the stable discrete orthogonal Hahn polynomials (DOHPs), which significantly reduces numerical defects. The other is the 3D image cuboid representation (ICR) to accelerate the computation time of discrete orthogonal Hahn moments (DOHMs) and improve the quality of 3D image reconstruction. The results of the simulation confirm the ability of the 3D image reconstruction using the proposed process. IntroductionResearch on 3D image reconstruction has always been a difficult challenge. The latter can be used for evaluating an object's 3D profile, as well as for defining a profile 3D coordinate at any given point. The reconstruction of 3D objects constitutes a

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“…Differential evolution (DE), first proposed by Storn and Price (Storn and Price, 1997), is a simple yet powerful evolutionary algorithm. DE has exhibited notable performance due to its simple structure, rapid convergence speed as well as strong robustness and has been applied successfully in many domains of science and engineering such as neural network (Su et al, 2019;Baioletti et al, 2020), power system (Sakr et al, 2017;Reddy and Bijwe, 2019), medical aspect (Nunes et al, 2017;Song et al, 2019;Hosny et al, 2020), image processing (Paul and Das, 2015;Tarkhaneh and Shen, 2019) and many other practical optimization problems (Balamurugan and Muthukumar, 2019;Huang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Dual Mutations Collaboration Mechanism with Elites Guiding and Inferiors Eliminating Techniques for Differential Evolution

Sun

et al. 2021

Preprint

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Differential Evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.

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Efficient compression of volumetric medical images using Legendre moments and differential evolution

Cited by 29 publications

References 25 publications

Optimal reconstruction and compression of signals and images by Hahn moments and artificial bee Colony (ABC) algorithm

Optimal reconstruction and compression of signals and images by Hahn moments and artificial bee Colony (ABC) algorithm

Partial 3D Image Reconstruction by Cuboids Using Stable Computation of Hahn Polynomials

Dual Mutations Collaboration Mechanism with Elites Guiding and Inferiors Eliminating Techniques for Differential Evolution

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