A context sensitive Masi entropy for multilevel image segmentation using moth swarm algorithm

Bhandari, Ashish Kumar; Rahul, Kusuma

doi:10.1016/j.infrared.2019.03.010

Cited by 38 publications

(8 citation statements)

References 35 publications

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“…If the thresholding is based on various threshold levels, known as multilevel thresholding, because of the presence of more than one threshold levels in the multi-level phenomenon, intricacy rises and precision reduces during the searching process [ 49 ]. In general, image thresholding approaches are based on parametric and non-parametric processes [ 50 , 51 ]. The parametric approach depends on the probability density function (PDF) for choosing a region with a larger computational cost.…”

Section: Proposed Methodsmentioning

confidence: 99%

A transfer learning based deep learning model to diagnose covid-19 CT scan images

2022

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To save the life of human beings during the pandemic conditions we need an effective automated method to deal with this situation. In pandemic conditions when the available resources becomes insufficient to handle the patient’s load, then we needed some fast and reliable method which analyse the patient medical data with high efficiency and accuracy within time limitations. In this manuscript, an effective and efficient method is proposed for exact diagnosis of the patient whether it is coronavirus disease-2019 (covid-19) positive or negative with the help of deep learning. To find the correct diagnosis with high accuracy we use pre-processed segmented images for the analysis with deep learning. In the first step the X-ray image or computed tomography (CT) of a covid-19 infected person is analysed with various schemes of image segmentation like simple thresholding at 0.3, simple thresholding at 0.6, multiple thresholding (between 26–230) and Otsu’s algorithm. On comparative analysis of all these methods, it is found that the Otsu’s algorithm is a simple and optimum scheme to improve the segmented outcome of binary image for the diagnosis point of view. Otsu’s segmentation scheme gives more precise values in comparison to other methods on the scale of various image quality parameters like accuracy, sensitivity, f-measure, precision, and specificity. For image classification here we use Resnet-50, MobileNet and VGG-16 models of deep learning which gives accuracy 70.24%, 72.95% and 83.18% respectively with non-segmented CT scan images and 75.08%, 80.12% and 99.28% respectively with Otsu’s segmented CT scan images. On a comparative study we find that the VGG-16 models with CT scan image segmented with Otsu’s segmentation gives very high accuracy of 99.28%. On the basis of the diagnosis of the patient firstly we go for an arterial blood gas (ABG) analysis and then on the behalf of this diagnosis and ABG report, the severity level of the patient can be decided and according to this severity level, proper treatment protocols can be followed immediately to save the patient's life. Compared with the existing works, our deep learning based novel method reduces the complexity, takes much less time and has a greater accuracy for exact diagnosis of coronavirus disease-2019 (covid-19).

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Section: Proposed Methodsmentioning

confidence: 99%

A transfer learning based deep learning model to diagnose covid-19 CT scan images

2022

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“…Further, HACLFOA is compared with PSO, FOA, QPSO and IDPSO Fitness values and Computation time The proposed HACLFOA has strongest global convergence ability among the compared algorithms thereby have a great potential in the image processing field 65 Thresholding Heuristic (TH) embedded into WOA, GWO and PSO (WOA-TH, GWO-TH, and PSO-TH) Otsu’s thresholding Bohat and Arya ( 2019 ) Standard Gray Scale Images Proposed method is compared with their respective base algorithm WOA, GWO and PSO Mean Fitness values, MSSIM and Mean Execution Time The proposed WOA-TH, GWO-TH and PSO-TH algorithms are better with improved computational time when compared with their respective base algorithm 66 Adaptive Differential Evolution with Levy Distribution (ALDE) Otsu’s thresholding Tarkhaneh and Shen ( 2019 ) MRI: Medical Images Proposed method is compared with SDE, BDE and hjDE PSNR and SSIM The proposed ALDE when equated with the benchmark algorithm performs better and has the capability to attain optimal threshold at a judicious computational cost 67 Sigmoid based optimal threshold selection technique with Differential Evolution (DE) and Tsallis Fuzzy Tsallis-Fuzzy Entropy Raj et al ( 2019 ) Standard Color Images Proposed method is compared with PLBA, BFO, MBFO and BA PSNR, SSIM, SNR and CPU time The proposed method is more stable and converges to optimal thresholds much faster. Standard deviation values further suggest that the proposed method is highly robust when compared with other algorithms 68 Energy-Masi using Moth Swarm Algorithm (MASI-ENG-MSA) Energy-Masi entropy Bhandari and Rahul ( 2019 ) Standard Color Images Proposed method is compared with MASI-DA, MASI-SCA, MASI-WOA, MASI-GOA, MASI-MSA, MASI-ENG-DA, MASI-ENG-SCA, MASI-ENG-WOA and MASI-ENG-GOA ME, Entropy, MSE, PSNR, SSIM and FSIM The proposed MASI-ENG-MSA in regard to the parameters such as threshold quality and computational cost outperforms other algorithms …”

Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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“…Ref. [ 27 ] employs the moth swarm algorithm (MSA) for image thresholding based on context-sensitive energy and Masi entropy and shows that it can outperform several PBMHs. Other PBMHs including multi-verse optimiser (MVO) [ 28 , 29 ], Harris hawks optimisation (HHO) [ 21 , 30 ], cuttlefish algorithm (CA) [ 31 ], and barnacles mating optimiser (BMO) [ 32 ] have also been employed for Masi entropy-based MLIT problems.…”

Section: Introductionmentioning

confidence: 99%

A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation

Mousavirad

Zabihzadeh

Oliva

et al. 2021

Entropy

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Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.

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A context sensitive Masi entropy for multilevel image segmentation using moth swarm algorithm

Cited by 38 publications

References 35 publications

A transfer learning based deep learning model to diagnose covid-19 CT scan images

A transfer learning based deep learning model to diagnose covid-19 CT scan images

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation

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