An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

Jiao, Wentan; Chen, Wenqing; Zhang, Jing

doi:10.1155/2021/6036410

Cited by 7 publications

(11 citation statements)

References 19 publications

(19 reference statements)

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“…Singing instead of simple random walks, Lévy flights boost search capabilities. Lévy flight is a heavy-tailed probability distribution-based random walk in step-lengths [38,39]. In CS, there are three fundamental idealised laws.…”

Section: Cuckoo Search (Cs) Algorithmmentioning

confidence: 99%

Fusion Strategy for Improving Medical Image Segmentation

Alraddady¹,

Zanaty²,

bakr³

et al. 2023

Computers, Materials &Amp; Continua

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In this paper, we combine decision fusion methods with four metaheuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar membership values, we apply the different rules of incorporation for these groups. The proposed approach is applied to challenging applications: MRI images, gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical images. It is shown that the proposed method gives accurate results; due to the decision fusion produces the greatest improvement in classification accuracy.

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Section: Cuckoo Search (Cs) Algorithmmentioning

confidence: 99%

Fusion Strategy for Improving Medical Image Segmentation

Alraddady¹,

Zanaty²,

bakr³

et al. 2023

Computers, Materials &Amp; Continua

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“…The gray value of pixels may change by noise and produce an imbalance in the histogram of an original image, so the bottom of a histogram may be filled to generate new peaks [7]. Hence, the bottom of the gray level valley between the two peaks can be segmented by multi-threshold [9,10,13].…”

Section: Related Workmentioning

confidence: 99%

“…For a given image, the optimal threshold can often be determined by analyzing its histogram. For example, when the histogram clearly shows a bimodal pattern, the midpoint of the two peaks can be selected as the optimal threshold [10]. According to the dimension, the image histogram can be classified into one-dimensional (1-D) histogram, two-dimensional (2-D) histogram, three-dimensional (3-D) histogram [3,11], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, swarm intelligence is a hotspot that simulates the behaviors of a swarm of animals in foraging and living. For example, the whale optimization algorithm (WOA) [1], Harris hawks optimization (HHO) [2], artificial neural networks (ANNs) [3,11], deep learning (DL) [4,12], gray wolf optimization (GWO) [5], particle swarm optimization (PSO) [7], differential evolution algorithm (DEA) [9], cuckoo search algorithm (CSA) [10], ant colony optimization (ACO) [13], genetic algorithm (GA) [14], artificial bee colony algorithm (ABC) [15], sparrow search algorithm (SSA) [16], and so on. Recent works verified that swarm intelligence algorithms can obtain good performance and are promising to solve image segmentation problems.…”

Section: Introductionmentioning

confidence: 99%

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Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Cai,

Li,

Zhang

et al. 2023

Applied Sciences

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Traditional artificial intelligence algorithms are prone to falling into local optima when solving threshold segmentation problems. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us solve the difficult problem. First, the algorithm methodology of an artificial Physarum polycephalum colony algorithm is described to search for the optimal solutions by expansion and contraction of a lot of artificial hyphae. Different artificial Physarum polycephalum can learn from each other and produce more hyphae in expansion. In contraction, the artificial Physarum polycephalum colony can select the best hyphae with high fitness through a quick sort algorithm, but the other hyphae with low fitness will be absorbed and disappear. Second, a fitness function is modeled based on Kapur’s entropy for the proposed artificial Physarum polycephalum colony algorithm to search for optimal threshold segmentation solutions. Third, a series of benchmark experiments are implemented to test the proposed artificial Physarum polycephalum colony algorithm, and some state-of-the-art approaches are employed for comparison. The experimental results verified that the proposed algorithm can obtain better accuracy and convergence speed, and is not easier to fall into the local optimal solution too early.

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“…Let the image set be A, and the non-empty subset be N with . However, these subsets can satisfy the following constraints [ 30 ].…”

Section: Proposed Approachmentioning

confidence: 99%

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Vinayaki

Kalaiselvi

2022

Neural Process Lett

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One of the most common complications of diabetes mellitus is diabetic retinopathy (DR), which produces lesions on the retina. A novel framework for DR detection and classification was proposed in this study. The proposed work includes four stages: pre-processing, segmentation, feature extraction, and classification. Initially, the image pre-processing is performed and after that, the Multi threshold-based Remora Optimization (MTRO) algorithm performs the vessel segmentation. The feature extraction and classification process are done by using a Region-based Convolution Neural Network (R-CNN) with Wild Geese Algorithm (WGA). Finally, the proposed R-CNN with WGA effectively classifies the different stages of DR including Non-DR, Proliferative DR, Severe, Moderate DR, Mild DR. The experimental images were collected from the DRIVE database, and the proposed framework exhibited superior DR detection performance. Compared to other existing methods like fully convolutional deep neural network (FCDNN), genetic-search feature selection (GSFS), Convolutional Neural Networks (CNN), and deep learning (DL) techniques, the proposed R-CNN with WGA provided 95.42% accuracy, 93.10% specificity, 93.20% sensitivity, and 98.28% F-score results.

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An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

Cited by 7 publications

References 19 publications

Fusion Strategy for Improving Medical Image Segmentation

Fusion Strategy for Improving Medical Image Segmentation

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

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