A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding

Maitra, Madhubanti; Chatterjee, Amitava

doi:10.1016/j.eswa.2007.01.002

Cited by 248 publications

(66 citation statements)

References 24 publications

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“…Threshold values and number of class's parameters mainly affects the segmented output [5]. Sridevi.M et al has put forward a novel methodology utilizing evolutionary computing to select optimum threshold value.…”

Section: Fig 1:-workflow Representation Of Multilevel Image Thresholmentioning

confidence: 99%

Multilevel Image Thresholding Using Artificial Intelligence Techniques.

Sabeena¹,

Subassree²

2017

IJAR

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Section: Fig 1:-workflow Representation Of Multilevel Image Thresholmentioning

confidence: 99%

Multilevel Image Thresholding Using Artificial Intelligence Techniques.

Sabeena¹,

Subassree²

2017

IJAR

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“…The used tissue membrane system searches the optimal segmentation parameters for optimal image thresholding problem (11) by the evolution of objects in cells. Several evolution cells are designed to co-evolve objects in the system, thus this will accelerate the exploitation of optimal segmentation parameters.…”

Section: Step 2 Evolution Rulesmentioning

confidence: 99%

“…However, GA has some drawbacks such as slow convergence rate, premature convergence to local minima. Thus, PSO has been applied to multi-level thresholding [10][11][12]. In addition, Tao et al [13] used the ACO to obtain the optimal parameters of the presented entropy-based object segmentation method.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the total fuzzy entropy is computed as folows (10) Based on maximum fuzzy entropy principle, the threelevel image thresholding problem can be formulated as an optimization (maximization) problem where the total fuzzy entropy is used for the objective function (11) where w=(a,b,c,d,e) denotes the parameters to be optimized. In this paper, membrane systems will be used to optimize these parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Method Based on Tissue Membrane Systems and Velocity-Position Model for Image Thresholding

Yi¹,

Zhang²,

Peng³

2014

Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-This paper proposes an image thresholding segmentation method, which combines tissue membrane systems and velocity-position model. A tissue membrane system is used as its computing framework and an improved velocity-position model is integrated as evolution rules of objects in cells. Due to parallel computing ability and inherent evolution-communication mechanism of the tissue membrane system, the presented hybrid method can effectively and efficiently find the optimal thresholds for three-level thresholding based on total fuzzy entropy. The performance of the presented hybrid method is studied with several evolutionary algorithms. Simulation results show that the presented hybrid method is superior or comparable to the other evolutionary algorithms and can be efficiently used for image thresholding.

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“…In 1999 Yin PY applied a variant of a genetic algorithm and embedded a learning strategy to enhance its searching ability for multilevel thresholding [2]; In 2007 Cheng proposed a cell-based two-region competition algorithm to define boundaries in two-dimensional ultrasound images [3]. In 2008 Maitra inserted a cooperative learning operator and comprehensive learning operator into a particle swarm algorithm [4], strengthening its segmentation ability significantly; Cheng used cell competition algorithms for breast lesion demarcation in 2010 [5]. In 2014 Feng Zhao proposed a multi-objective spatial fuzzy clustering algorithm [6] for segmentation, and simulation results show a high level of effectiveness [7].…”

Section: Introductionmentioning

confidence: 99%

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation

Wang

Zhou

Zhao

et al. 2015

BME

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Abstract. Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

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A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding

Cited by 248 publications

References 24 publications

Multilevel Image Thresholding Using Artificial Intelligence Techniques.

Multilevel Image Thresholding Using Artificial Intelligence Techniques.

A Hybrid Method Based on Tissue Membrane Systems and Velocity-Position Model for Image Thresholding

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation

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