Nature and Biologically Inspired Image Segmentation Techniques

Singh, Simrandeep; Mittal, Nitin; Thakur, Diksha; Singh, Harbinder; Oliva, Diego; Demin, Anton Yurievich

doi:10.1007/s11831-021-09619-1

Cited by 20 publications

(10 citation statements)

References 134 publications

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“…In each iteration of COA, when the population update is completed, the best coyote and the worst coyote are updated respectively through Eqs. (19) and (21), and the relatively better individuals are selected through Eqs. (20) and (22).…”

Section: Worst Coyote Reverse Learning Strategymentioning

confidence: 99%

“…At the same time, the author also experimentally analyzes the quantitative statistical performance in image thresholding. Singh et al [21] summarized nearly 30 kinds of NIMA, including DE, FA, GA, PSO, ABC from 2005 to 2021. Compared with ANN (artificial neural networks), growing region, edge-based algorithms and other kinds, thresholding method has been widely used because it requires less prior knowledge and minimal steps.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Li¹,

Huang²,

Qian³

et al. 2022

Computers, Materials &Amp; Continua

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In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified. Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence. Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm. Therefore, in this paper we first perform chaotic initialization, using chaotic sequence to replace random number initialization in standard COA. By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy, a hybrid reverse learning strategy is then formed. In the process of algorithm traversal, the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively, which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence. Based on the above improvements, the coyote optimization algorithm has better global convergence and computational robustness. The simulation results show that the algorithm has better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

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Section: Worst Coyote Reverse Learning Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Li¹,

Huang²,

Qian³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In the related literature, metaheuristic algorithms and their improved versions are used for multi-level segmentation. Such as artificial bee colony (ABC) [44], genetic algorithms (GA) [40], honey bee mating optimization (HBMO) [17], modified firefly algorithm (MFA) [15], particle swarm optimization (PSO) [30], bacterial foraging algorithm (BFA) [45], differential evolution (DE) algorithms [25], wind-driven optimization(WDO) [24], cuckoo search (CS) algorithms [31], ant colony algorithm (ACO) [11], grasshopper optimization algorithm (GOA) [27], self-adaptive parameter optimization (SAPO) [8], electromagnetism-like optimization (EMO) algorithm [16], and glowworm swarm optimization (GSO) algorithms [29]. Some other interesting approaches that successfully segment digital images are based on modern MA as the Coronavirus Optimization Algorithm combined with Harris Hawks Optimizer [18], the improved modified Differential Evolution (MDE) [35], the directional mutation and crossover boosted ant colony optimization (XMACO) [34], the Harris Hawks Optimizer (HHO) [36], the Mutated Electromagnetic Field Optimization (MEFO) [4] or the Altruistic Harris Hawks Optimizer [5].…”

Section: Introductionmentioning

confidence: 99%

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

Singh

Mittal

Singh

et al. 2023

Multimed Tools Appl

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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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“…Recently, nature-inspired and heuristic optimization algorithms have been successfully adopted for various applications of image segmentation [15,16]. For example, the Ant lion algorithm, which simulates predators hunting ants, was used to scan over X-ray image for possible degenerated tissues [17].…”

Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

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Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

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Nature and Biologically Inspired Image Segmentation Techniques

Cited by 20 publications

References 134 publications

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

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

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

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