Multilevel thresholding for crop image segmentation based on recursive minimum cross entropy using a swarm-based technique

Kumar, Arun; Vishwakarma, Amit; Singh, Girish Kumar

doi:10.1016/j.compag.2022.107488

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…Based on Kullback-Leibler divergence, Li and Lee proposed the well-known minimum cross entropy image thresholding method 27 , which has been successfully applied in various fields. For complex scenarios, the effectiveness of this method was also confirmed through multilevel expansion 14 , 28 .…”

Section: Related Workmentioning

confidence: 80%

“…DeepCrack is a public benchmark dataset with cracks in multiple scales and scenes to evaluate the crack detection systems. In our work, we conducted multiple tests on this dataset to compare the performance of the proposed algorithm with other cutting-edge methods for image multilevel thresholding, including particle swarm optimization (PSO) 15 , butterfly optimization (BFO) 16 , gases Brownian motion optimization (GBMO) 16 , exchange market algorithm (EMA) 17 , modified whale optimization algorithm (MWOA) 18 , hybrid whale optimization algorithm (HWOA) 19 , and cuckoo search optimization (CSO) 14 .…”

Section: Experimental Environment and Evaluation Metricsmentioning

confidence: 99%

“…Therefore, numerous studies have developed various multilevel thresholding methods to enhance image segmentation performance and enable practical applications. For example, multilevel thresholding has been widely and successfully applied in areas such as medical diagnosis 11 , 12 , power equipment fault detection 13 , and crop image segmentation 14 .…”

Section: Introductionmentioning

confidence: 99%

“…In practical applications, the acquisition of the final image segmentation results is also strongly influenced by the choice of the criterion functions. The commonly used criterion functions include the Otsu criterion 21 , 22 based on the maximum inter-class variance, the maximum Shannon entropy criterion 23 , 24 , and the minimum cross entropy criterion 14 , 25 , etc. For the Otsu criterion, the maximum Shannon entropy criterion, and the minimum cross entropy criterion, they have similar properties.…”

Section: Introductionmentioning

confidence: 99%

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Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Nie,

Liu,

Zhang

2024

Sci Rep

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Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

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Section: Related Workmentioning

confidence: 80%

Section: Experimental Environment and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Nie,

Liu,

Zhang

2024

Sci Rep

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“…Kumar et al [20] Efficient cuckoo search algorithm improved an efficient version of the Whale Optimization Algorithm with a chaotic random mutation strategy and Levy operator [16]. In addition to skin cancer images, metaheuristic methods are used in the segmentation of various medical images.…”

Section:  Breast Cancer  Skin Cancer Imagesmentioning

confidence: 99%

Improved PSO With Visit Table and Multiple Direction Search Strategies for Skin Cancer Image Segmentation

Olmez,

Koca,

Sengür

et al. 2024

IEEE Access

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Automated screening is employed to assist skin specialists in accurately detecting skin lesions at an early stage. Multilevel thresholding is a widely popular and efficient technique for enhancing the classification of skin cancer images. This paper proposes improved PSO with a novel visit table and multiple directions search strategies to develop the performance of the multilevel thresholding. A visit table strategy has been developed that prevents unnecessary searches of the original particle swarm optimization (PSO) algorithm by allowing the discovery of new points by making fewer visits to frequently visited points and their neighbors. Besides, a multiple directions search strategy has been introduced for the PSO to increase the diversity of the population and overcome the stuck at the local optimum by enhancing exploration ability. The qualitative, quantitative, and scalability analyzes of the improved PSO (IPSO) method were carried out on 50 benchmark functions and the highest performance was achieved with the proposed method in most of these functions. Secondly, a multilevel image segmentation application is presented on skin cancer images using two-dimensional (2D) non-local means histograms, improved PSO and Renyi's entropy. In this work, the ISIC 2017 skin cancer image dataset is used for segmentation application and various performance evaluation metrics are used. The obtained results are compared with seven state-of-the-art approaches to show the efficiency of the proposed approach. It can be noted from the obtained results that, the proposed method outperforms the compared method based on the average of evaluation metrics for all skin cancer images. The best results in SSIM value of 0.8285, FSIM value of 0.7332, and PSNR value of 19.0576 are achieved by using the proposed method in skin cancer image segmentation. Hence, our proposed method is ready to be tested with huge databases and can aid skin specialists in making an accurate diagnosis. INDEX TERMSMultilevel thresholding, multiple directions search strategy, PSO, skin cancer, visit table strategy.

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A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing

Amiriebrahimabadi,

Rouhi,

Mansouri

2024

Arch Computat Methods Eng

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Multilevel thresholding for crop image segmentation based on recursive minimum cross entropy using a swarm-based technique

Cited by 13 publications

References 28 publications

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Improved PSO With Visit Table and Multiple Direction Search Strategies for Skin Cancer Image Segmentation

A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing

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