Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

Aranguren, Itzel; Valdivia, Arturo; Pérez‐Cisneros, Marco; Oliva, Diego; Osuna-Enciso, Valentín

doi:10.1007/s11042-022-11959-4

Cited by 9 publications

(7 citation statements)

References 53 publications

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“…It is studied from the literature that the multilevel thresholding (MTH) is the easiest way of achieving multiclass segmented outputs. In this context, many methodologies are reported [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. Most of these methods use the image histogram-based entropy values.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the recent techniques [5], [6], [7], [8], [9], [10], [11], [12] are, more or less, concentrated on the use of the entropic values. The authors in [5] have presented a MTH method based on 2D histogram.…”

Section: Introductionmentioning

confidence: 99%

“…However, the method was similar to the earlier approaches. The authors in [8], [9], and [10] used the 2D Rényi entropy as an objective function for solving the MTH problem. However a 2D histogram is constructed using the lateral inhibition in [10].…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [8], [9], and [10] used the 2D Rényi entropy as an objective function for solving the MTH problem. However a 2D histogram is constructed using the lateral inhibition in [10]. Non-extensive entropy based MTH was presented in [11].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the accuracy value is lessened. In addition, the local averaging adopted while constructing the 2D histogram in the above methods [5], [6], [7], [8], [9], [10], [11], [12] causes loss of the edge information, leading to less accuracy. This is the reason why the authors are motivated to suggest a novel decisive kind of entropy based objective function, coined as practical decisive row class entropy (PDRCE).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

Panda

Swain²,

Naik

et al. 2022

IEEE Access

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One of today's inspiring issues is the 2D histogram-based multilevel threshold selection which is used for segmenting images into several regions. The image analysis warrants exploration of multiclass thresholding techniques using various entropy-based objective functions. In this context, the Shannon type of entropic function without inherent decision making capacity has been widely used for threshold selection in the last decade. Furthermore, a 2D histogram was constructed using local average intensity values resulting in loss of some edge information. To address these problems, this study proposes a new methodology using a novel practical decisive row-class entropy (PDRCE) based fitness function for multilevel thresholding. The PDRCE values are computed using the newly constructed 2D histogram-based on normal local variance. Further, an opposition flow directional algorithm (OFDA) is proposed to maximize the fitness function. The performance of the proposed technique is compared with five state-of-the-art 2D histogram-based entropic fitness functions. Moreover, the performance of OFDA is investigated through comparison with other global optimizers namely the genetic algorithm, particle swarm optimization and artificial bee colony. An image segmentation evaluation dataset (BSDS500) is used in this experiment. It is witnessed that the proposal is more efficient than state-of-the-art methods. Our fitness function would be useful for registration, segmentation, fusion, etc.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

Panda

Swain²,

Naik

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Chaotic electromagnetic field optimization

Ibrahim

Tawhid

2022

Artif Intell Rev

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Improving the segmentation of digital images by using a modified Otsu’s between-class variance

Singh

Mittal

Singh

et al. 2023

Multimed Tools Appl

Self Cite

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Image segmentation is a critical stage in the analysis and pre-processing of images. It comprises dividing the pixels according to threshold values into several segments depending on their intensity levels. Selecting the best threshold values is the most challenging task in segmentation. Because of their simplicity, resilience, reduced convergence time, and accuracy, standard multi-level thresholding (MT) approaches are more effective than bi-level thresholding methods. With increasing thresholds, computer complexity grows exponentially. A considerable number of metaheuristics were used to optimize these problems. One of the best image segmentation methods is Otsu's between-class variance. It maximizes the between-class variance to determine image threshold values. In this manuscript, a new modified Otsu function is proposed that hybridizes the concept of Otsu's between class variance and Kapur's entropy. For Kapur's entropy, a threshold value of an image is selected by maximizing the entropy of the object and background pixels. The proposed modified Otsu technique combines the ability to find an optimal threshold that maximizes the overall entropy from Kapur's and the maximum variance Multimedia Tools and Applications

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Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

Cited by 9 publications

References 53 publications

A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

Chaotic electromagnetic field optimization

Improving the segmentation of digital images by using a modified Otsu’s between-class variance

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