An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation

Tarkhaneh, Omid; Shen, Haifeng

doi:10.1016/j.eswa.2019.07.037

Cited by 99 publications

(34 citation statements)

References 54 publications

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“…So far, DE variants have been applied to various fields, such as target allocation [41], text classification [42], image segmentation [43], and neural network [44][45][46][47]. For the future work, the proposed DSIDE algorithm will be applied to the parameter optimization of neural network and may further apply it to the air traffic control system for flight trajectory prediction [48,49].…”

Section: Discussionmentioning

confidence: 99%

An Improved Differential Evolution Algorithm Based on Dual-Strategy

Zhong

Cheng

2020

Mathematical Problems in Engineering

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In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization problems over continuous space and has been widely used in many fields of science and engineering. How to avoid the local optimal solution and how to improve the convergence performance of DE are hotpot problems for many researchers. In this paper, an improved differential evolution algorithm based on dual-strategy (DSIDE) is proposed. The DSIDE algorithm has two strategies. (1) An enhanced mutation strategy based on “DE/rand/1,” which takes into account the influence of reference individuals on mutation and has strong global exploration and convergence ability. (2) A novel adaptive strategy for scaling factor and crossover probability based on fitness value has a positive impact on population diversity. The DSIDE algorithm is verified with other seven state-of-the-art DE variants under 30 benchmark functions. Furthermore, Wilcoxon sign rank-sum test, Friedman test, and Kruskal–Wallis test are utilized to analyze the results. The experiment results show that the proposed DSIDE algorithm can significantly improve the global optimization performance.

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Section: Discussionmentioning

confidence: 99%

An Improved Differential Evolution Algorithm Based on Dual-Strategy

Zhong

Cheng

2020

Mathematical Problems in Engineering

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“…The k-means algorithm [8], fuzzy c-means (FCM) [2,9] and self-organizing mapping (SOM) algorithm have been popular clustering techniques with their own advantages and disadvantages [2,9]. Researchers have also reported the application of meta-heuristic algorithms such as genetic algorithm (GA) [10,11], artificial bee colony (ABC) [12][13][14], ant colony optimization (ACO) [15,16], particle swarm optimization (PSO) [17] and differential evolution algorithm (DE) [18] to cluster and/or segment medical images [19].…”

Section: Introductionmentioning

confidence: 99%

“…18) is used to calculate the accuracy where the only parameter not described is True Negative (TN) representing the number of samples correctly identified as normal. Finally, the error rate of the proposed method is calculated based on the Eq (18)…”

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confidence: 99%

EL-IGrowCut: MRI Brain Tissue Segmentation Based on Ensemble Learning and Improve GrowCut Techniques

Jafari¹,

Ghaffari²

2021

Preprint

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Background and ObjectiveMagnetic resonance (MR) technology enables physicians to employ digital imaging as a tool to identify and analyze brain tumors as well as to differentiate between healthy and tumor tissues. Since precisely defining the tumor position is a challenging task, segmentation is necessary stage in medical image processing. Segmentation is defined as the division of a given image into non-overlapping regions with relatively similar features. Recently, various techniques have been proposed for MR image segmentation. MethodsIn this paper, a new ensemble clustering technique based on neural network algorithm (NNA) was introduced for segmentation of MRI Brain tissues, which consists of two main phases: training phase and testing phase. In the former, pixels with the same amount of light intensity were put into one group via simple linear iterative clustering (SLIC) algorithm, creating superpixels. Then, the combination of GrowCut methods and Zernik’s feature extraction method and clustering and classification ensemble learning, including multi-layer perceptron neural network algorithm (MLP), Support vector machine (SVM) and K nearest neighbor (KNN), are based on geometric features in order to detect the division of MRI Brain tissues. The improved GrowCut method, based on the geometric properties of the images, has been used to segmentation the image of the brain from MRI images. Then, with the help of Zernik’s technique, image features were extracted based on the features of the edges and the texture of the images. Finally, the ensemble learning methods such as KNN, SVM, and MLP combine together for attempted to detect MRI Brain tissues. Results and ConclusionsThe performance of the proposed method was evaluated on the IBSR20 dataset and the obtained results were compared separately with the six basic clustering methods (that together are creating the ensemble clustering technique). The results demonstrated that the proposed technique outperforms other clustering algorithms in terms of segmentation accuracy in most of the experimental cases. Therefore, by presenting experiments to diagnose MRI Brain tissues, it was observed that the proposed method detected the MR with acceptable accuracy and the combination of ensemble learning methods and GrowCut and Zernik’s feature extraction methods was successful.

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“…The combination of Archimedean spiral and Levy flight maintains the balance between the exploration and development capabilities of the differential evolution algorithm while increasing the convergence speed; In the same year, Omid combined Levy Flight and Coates spiral with the differential evolution algorithm [32], and used the Cauchy distribution to improve the global search range and further strike a balance between development and exploration; Mohammed Azmi Al-Betar et al utilized the island model in the evolution process of FPA to control diversity. The proposed approach is called IsFPA [33].In 2019, Seyed and Samaneh combined the whale optimization algorithm with the differential evolution algorithm [34] and proposed an improved whale optimization algorithm, which improved the exploration ability of the algorithm and overcame the shortcomings of the original whale optimization algorithm that prematurely converged and easily fell into local optimization; In the same year, Chen et al improved the firework algorithm [35], optimized the explosion scheme of the original algorithm, and added the GS-Gaussian explosion operator and deep information exchange strategy.…”

Section: Introductionmentioning

confidence: 99%