Multilevel segmentation of Hippocampus images using global steered quantum inspired firefly algorithm

Choudhury, Alokeparna; Samanta, Sourav; Pratihar, Sanjoy; Bandyopadhyay, Oishila

doi:10.1007/s10489-021-02688-6

Cited by 18 publications

(9 citation statements)

References 83 publications

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“…Then, at intensity level g (0 ≤ g ≤ L−1) of image I, the energy function value is calculated. The energy function can be expressed as [11,12]:…”

Section: Energy Curve-otsu Methodsmentioning

confidence: 99%

“…L was considered to be 256 in this paper. Multilevel thresholding determines the multiple thresholds and develops an output image with multiple groups as follows [12]:…”

Section: Multilevel Thresholding Functionsmentioning

confidence: 99%

“…The objective function of metaheuristic algorithms is a criterion used to determine the optimality of the obtained solutions. Over the last decade, the use of entropy as an objective function has attracted much attention [11][12][13][14][15][16]. These parametric approaches attempt to estimate the parameters of distribution that will best fit the given histogram.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Imagery Segmentation: A Hybrid Approach

et al. 2021

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In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

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“…Then, at intensity level g (0 ≤ g ≤ L−1) of image I, the energy function value is calculated. The energy function can be expressed as [11,12]:…”

Section: Energy Curve-otsu Methodsmentioning

confidence: 99%

“…L was considered to be 256 in this paper. Multilevel thresholding determines the multiple thresholds and develops an output image with multiple groups as follows [12]:…”

Section: Multilevel Thresholding Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Imagery Segmentation: A Hybrid Approach

et al. 2021

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“…Medical images are a particular class of images, their feature space is usually more complex, and more attention is paid to the accuracy and efficiency of image segmentation[ 13 ]. In recent years, many research results have been published on applying the Meta-Heuristic Algorithm (MHA) to optimize MIS on medical images [ 14 – 16 ]. The advantages of MHA in medical image segmentation are becoming more and more prominent.…”

Section: Introductionmentioning

confidence: 99%

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

et al. 2023

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Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

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“…Some recent examples are as follows. In [4][5][6] quantum-inspired metaheuristics are applied to image processing problems, structural design applications are presented in [7][8][9][10]. In [11][12][13][14][15][16], quantum-inspired metaheuristics are utilized for job-scheduling problem, and some examples of networks applications of quantum-inspired metaheuristics are found in [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size

Hakemi

Houshmand

Hosseini

2022

Preprint

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This paper proposes a Dynamic Quantum-inspired Genetic Algorithm (DQGA), which combines the quantum mechanics concepts with metaheuristics principles to enhance the search capability of the genetic algorithm. The proposed algorithm also introduces the lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases. Apart from that, a novel adaptive look-up table for rotation gates is presented to boost the algorithm's optimization abilities. To evaluate the effectiveness of these ideas, the DQGA is tested by various mathematical benchmark functions as well as real-world constrained engineering problems against several well-known and state-of-the-art algorithms. The obtained results indicate the merits of the proposed algorithm.

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Multilevel segmentation of Hippocampus images using global steered quantum inspired firefly algorithm

Cited by 18 publications

References 83 publications

Remote Sensing Imagery Segmentation: A Hybrid Approach

Remote Sensing Imagery Segmentation: A Hybrid Approach

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size

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