Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease

Zhao, Songwei; Wang, Pengjun; Heidari, Ali Asghar; Chen, Huiling; Turabieh, Hamza; Mafarja, Majdi; Li, Chengye

doi:10.1016/j.compbiomed.2021.104427

Cited by 102 publications

(28 citation statements)

References 127 publications

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“…This entropy model has high computation complexity in solving multidimensional image segmentation problems. In [42,43], the authors showed the use of two-dimensional Rényi's entropy for the segmentation of general RGB images. In [44], the authors explored two-dimensional Rényi's entropy for image compression.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

“…The limiting case of Rényi entropy is when α reaches unity. The a priori Rényi entropy for each distribution [9,42,43] is represented as:…”

Section: Multilevel Rényi's Entropymentioning

confidence: 99%

Remote Sensing Imagery Segmentation: A Hybrid Approach

et al. 2021

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“…Complex feature spaces, especially in the medical image, are often highly challenging to handle [ 25 ]. Clinical analysis is regularly inspired by just a particular segment of a medical image, while different parts are of optional significance [ 26 ]. Hence more emphasis is required on the accuracy and efficiency of the method used to handle the issue [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Abdel-Basset et al [ 30 ] developed a new algorithm named HSMA_WOA integrating slime mould algorithm and WOA, also segmented COVID-19 chest X-ray images applying multilevel thresholding strategy. Zhao et al [ 26 ] proposed an improved slime mould algorithm (DASMA) with a diffusion mechanism and an association strategy to increase solution diversity and faster convergence speed, respectively. They applied the method to segment the CT image of chronic obstructive pulmonary disease (COPD) using multilevel thresholding approach.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

Chakraborty

Saha

Nama

et al. 2021

Computers in Biology and Medicine

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“…Although SMA is competitive compared to other algorithms, there are some shortcomings in SMA. Due to the shortcoming of diminished population diversity in SMA, it easily falls into local optimum [ 37 ]. The selection of update strategies by SMA weakens the exploration ability [ 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

A Modified Slime Mould Algorithm for Global Optimization

Tang

Han

et al. 2021

Computational Intelligence and Neuroscience

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Slime mould algorithm (SMA) is a population-based metaheuristic algorithm inspired by the phenomenon of slime mould oscillation. The SMA is competitive compared to other algorithms but still suffers from the disadvantages of unbalanced exploitation and exploration and is easy to fall into local optima. To address these shortcomings, an improved variant of SMA named MSMA is proposed in this paper. Firstly, a chaotic opposition-based learning strategy is used to enhance population diversity. Secondly, two adaptive parameter control strategies are proposed to balance exploitation and exploration. Finally, a spiral search strategy is used to help SMA get rid of local optimum. The superiority of MSMA is verified in 13 multidimensional test functions and 10 fixed-dimensional test functions. In addition, two engineering optimization problems are used to verify the potential of MSMA to solve real-world optimization problems. The simulation results show that the proposed MSMA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.

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Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease

Cited by 102 publications

References 127 publications

Remote Sensing Imagery Segmentation: A Hybrid Approach

Remote Sensing Imagery Segmentation: A Hybrid Approach

COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

A Modified Slime Mould Algorithm for Global Optimization

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