Optimal multilevel thresholding using bacterial foraging algorithm

Sathya, P. D.; Kayalvizhi, R.

doi:10.1016/j.eswa.2011.06.004

Cited by 165 publications

(43 citation statements)

References 15 publications

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“…In general, optimal thresholding methods are simple and effective in bi-level thresholding [7]. However, they are; including Otsu's unsupervised method; inefficient in determining the optimal thresholds due to the exponential growth in computation time in when dealing with multilevel thresholding problems [7].…”

Section: Introductionmentioning

confidence: 99%

An evaluation of particle swarm optimization techniques in segmentation of biomedical images

Lahmiri

Boukadoum

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Image segmentation is a common image processing step to many computer vision applications with the purpose to segment pixels into different classes. As improved variants of particle swarm optimization (PSO) algorithms, the fractional-order Darwinian particle swarm optimization (FODPSO) and Darwinian particle swarm optimization (DPSO) have been proposed for image segmentation. The purpose of this paper is to compare the segmentation performance of PSO, DPSO, and FODPSO as parametric approaches to existing methods; namely the parametric fuzzy c-means (FCM) algorithm, and the non-parametric Otsu segmentation technique with application to five biomedical images. All PSO-based experiments are conducted with twenty runs to assess the effectiveness of PSO models. The universal quality index is used to evaluate the segmentation results. The obtained experimental results showed that particle swarm based algorithms outperformed both FCM and Otsu segmentation technique.

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Section: Introductionmentioning

confidence: 99%

An evaluation of particle swarm optimization techniques in segmentation of biomedical images

Lahmiri

Boukadoum

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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“…The second is proposed by Otsu et al [13] and maximizes the between-class variance. Although the two methods have proved to be highly efficient for bi-level thresholding, the computational complexity for MT increases with each new threshold [16].…”

Section: Related Workmentioning

confidence: 99%

A Quick Artificial Bee Colony Algorithm for Image Thresholding

Sun

Guo

et al. 2017

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Abstract:The computational complexity grows exponentially for multi-level thresholding (MT) with the increase of the number of thresholds. Taking Kapur's entropy as the optimized objective function, the paper puts forward the modified quick artificial bee colony algorithm (MQABC), which employs a new distance strategy for neighborhood searches. The experimental results show that MQABC can search out the optimal thresholds efficiently, precisely, and speedily, and the thresholds are very close to the results examined by exhaustive searches. In comparison to the EMO (Electro-Magnetism optimization), which is based on Kapur's entropy, the classical ABC algorithm, and MDGWO (modified discrete grey wolf optimizer) respectively, the experimental results demonstrate that MQABC has exciting advantages over the latter three in terms of the running time in image thesholding, while maintaining the efficient segmentation quality.

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“…The second submitted by Kapur in [6] uses the maximization of the entropy to measure the homogeneity of the classes. Their efficiency and accuracy have been already proved for a bi-level segmentation [9]. Although both Otsu's and Kapur's can be expanded for multilevel thresholding, their computational complexity increases exponentially with each new threshold [9].…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Thresholding algorithm using electromagnetism optimization

et al. 2014

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Segmentation is one of the most important tasks in image processing. It consist in classify the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for multilevel thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a multilevel thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu's or Kapur's method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation.

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Optimal multilevel thresholding using bacterial foraging algorithm

Cited by 165 publications

References 15 publications

An evaluation of particle swarm optimization techniques in segmentation of biomedical images

An evaluation of particle swarm optimization techniques in segmentation of biomedical images

A Quick Artificial Bee Colony Algorithm for Image Thresholding

A Multilevel Thresholding algorithm using electromagnetism optimization

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