Image Segmentation Based on K-means and Genetic Algorithms

Khrissi, Lahbib; Akkad, Nabil El; Satori, Hassan; Satori, Khalid

doi:10.1007/978-981-15-0947-6_46

Cited by 25 publications

(13 citation statements)

References 8 publications

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“…The result of ACS is better than algorithm based several measurements such as PSNR, MSE, FSIM, SSIM, UIQI, and CPU time. KHRISSI et al [32] proposed method which used CSA with FCM. The CSA used to explore optimal centers of clusters then apply FCM on many images selected BSD300 database.…”

Section: Preceding Studies Of Using Cs In the Scope Of Image Processingmentioning

confidence: 99%

An overview of Cuckoo Optimization Algorithm based Image Processing

sulaiman

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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The Cuckoo Search (CS) algorithm is an effective swarm intelligence optimization algorithm whose important developments were presented by Yang and Deb in 2009. The CS algorithm has been used in many applications to solve optimization problems. This paper describes an overview of the applications of CS in the scope of image processing to solve optimization problems for the image during the years 2015-2021. The main categories reviewed that used CS in the field of image processing are: image segmentation, image optimization, image noise removal, image classification, feature extraction in images, image clustering and edge detection. The aim of this paper is to provide an overview and summarize the literature review of applying CS algorithm in these categories in order to extract which categories that applied this algorithm more than others. From this review we conclude that CS was mostly applied in the image segmentation category to optimize the threshold search.

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Section: Preceding Studies Of Using Cs In the Scope Of Image Processingmentioning

confidence: 99%

An overview of Cuckoo Optimization Algorithm based Image Processing

sulaiman

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…Next, based on minimizing the cost function (or increasing the fitness function), these obtained sequences are divided to separate parts and then each part adds to another part to create a new chain of spots within the searching space until stopping criteria are reached. The task of dividing and then adding sequences is done by considering their participation probability [68][69][70].…”

Section: Biomed Research Internationalmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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“…Ant colony optimization has been used by Khorram et al [17], and particle swarm optimization by Tan et al [18]. Genetic algorithms have been exploited by Khrissi et al [19]. Moreover, other metaheuristics and their hybridization have been proposed by Yue et al [20], and Karthikeyan et al [21] SCA is among the recent promising population-based metaheuristic algorithms described by Mirjalili in 2016.…”

Section: Introductionmentioning

confidence: 99%

A Performant Clustering Approach Based on An Improved Sine Cosine Algorithm

Khrissi¹,

Akkad²,

Satori³

et al. 2022

IJC

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Image segmentation is a fundamental and important step in many computer vision applications. One of the most widely used image segmentation techniques is clustering. It is a process of segmenting the intensities of a non-homogeneous image into homogeneous regions based on their similarity property. However, clustering methods require a prior initialization of random clustering centers and often converge to the local optimum, thanks to the choices of the initial centers, which is a major drawback. Therefore, to overcome this problem, we used the improved version of the sine-cosine algorithm to optimize the traditional clustering techniques to improve the image segmentation results. The proposed method provides better exploration of the search space compared to the original SCA algorithm which only focuses on the best solution to generate a new solution. The proposed ISCA algorithm is able to speed up the convergence and avoid falling into local optima by introducing two mechanisms that take into account the first is the given random position of the search space and the second is the position of the best solution found so far to balance the exploration and exploitation. The performance of the proposed approach was evaluated by comparing several clustering algorithms based on metaheuristics such as the original SCA, genetic algorithms (GA) and particle swarm optimization (PSO). Our evaluation results were analyzed based on the best fitness values of several metrics used in this paper, which demonstrates the high performance of the proposed approach that gives satisfactory results compared to other comparison methods.

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Image Segmentation Based on K-means and Genetic Algorithms

Cited by 25 publications

References 8 publications

An overview of Cuckoo Optimization Algorithm based Image Processing

An overview of Cuckoo Optimization Algorithm based Image Processing

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

A Performant Clustering Approach Based on An Improved Sine Cosine Algorithm

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