Image segmentation with Kapur, Otsu and minimum cross entropy based multilevel thresholding aided with cuckoo search algorithm

Kalyani, R.; Sathya, P. D.; Sakthivel, V.

doi:10.1088/1757-899x/1119/1/012019

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The optimization process hinges on the mean values within each region of the given image. Numerous entropy-based thresholding models have emerged to address data separation challenges [16][17][18]. The application of cross-entropy approaches has paved the way for advancements in image segmentation within the literature.…”

Section: Minimum Cross-entropy Thresholding (Mcet)mentioning

confidence: 99%

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

Jumiawi,

El-Zaart

2023

ASI

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In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.

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Section: Minimum Cross-entropy Thresholding (Mcet)mentioning

confidence: 99%

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

Jumiawi,

El-Zaart

2023

ASI

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“…For example, determining white matter (WM) and grey matter volume in brain magnetic resonance images (MRI) has become an important measurement tool for multiple sclerosis (MS) patient monitoring and research [3][4][5]. A number of different approaches for brain tissue segmentation have been identified in the literature, including histogram-based techniques, edge detection, regionbased segmentation [6,7], fuzzy clustering [8][9][10][11][12], graph cuts [13,14], genetic algorithms [15][16][17], threshold approaches [18][19][20][21], and hybrid techniques [22,23].…”

Section: Introductionmentioning

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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“…Numerical results clearly demonstrated that proposed ICS with Otsu objective function provided promising outcomes. Kalyani et. al.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Logic-Based Improved Cuckoo Search Algorithm

Dhal

Das

Gálvez

2022

International Journal of Applied Metaheuristic Computing

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Cuckoo Search (CS) algorithm is a nature-inspired optimization algorithm (NIOA) with less control parameters that is stable, versatile, and easy to implement. CS has good global search capabilities, but it is prone to local optima problems. As a result, it may be possible to improve the classic CS algorithm's optimization capability. Centered on fuzzy set theory, this paper introduces an improved CS version. The population of solutions has been divided into two fuzzy sets, and each solution is assigned to one of the sets based on its fitness. The fuzzy collection centroids, global best solution advice, and Lévy distribution dependent mutation are all used to boost the population's solutions. With well-accepted objective functions such as Otsu inter class variance and Kapur's entropy, the experimental analysis has been conducted on the CEC-2014 test suite and image multi-level thresholding domain. The proposed fuzzy cuckoo search (FCS) algorithm is compared to the classical CS, PSO, FA, SMA, and BA algorithm and provides satisfactory results.

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Image segmentation with Kapur, Otsu and minimum cross entropy based multilevel thresholding aided with cuckoo search algorithm

Cited by 6 publications

References 15 publications

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

Fusion Strategy for Improving Medical Image Segmentation

A Novel Fuzzy Logic-Based Improved Cuckoo Search Algorithm

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