Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm

Aranguren, Itzel; Valdivia, Arturo; Morales-Castañeda, Bernardo; Oliva, Diego; Elaziz, Mohamed Abd; Pérez‐Cisneros, Marco

doi:10.1016/j.bspc.2020.102259

Cited by 39 publications

(14 citation statements)

References 60 publications

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“…Table 1 summarizes some optimization algorithms used for image segmentation. Moreover, several researchers work on medical imaging segmentation using optimization algorithms; for example, in [ 52 ], the authors proposed a method MRI image segmentation using the LASHED optimization algorithm. The statistically verified results demonstrate that the suggested approach improves consistency and segmentation quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Houssein

Emam

Ali

2021

Neural Comput & Applic

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Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people’s safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO’s initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu’s method for all the used metrics.

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Section: Literature Reviewmentioning

confidence: 99%

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Houssein

Emam

Ali

2021

Neural Comput & Applic

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“…Oliva et al [9] proposed an adaptive differential evolution and linear population size reduction (LSHADE) metaheuristic algorithm to determine the optimal threshold value by employing the minimum cross-entropy as a fitness function for the segmentation of brain tissue from MR images.…”

Section: Thresholdingmentioning

confidence: 99%

Brain Image Segmentation in Recent Years: A Narrative Review

2021

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Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions. This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images. Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present review. Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods. From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors. However, these methods fall behind in terms of computation and memory complexity.

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“…Every patient might have different observed data, and the interpretation of the data depends on the experience of those skilled in the art, this can lead to errors within and between observers [77]. Segmentation ensues by dividing digital images into multiple segments into nonoverlapped areas that share characteristics such as shape, intensity, or texture to locate and identify objects and boundaries in an image [18], [78]- [89]. In further parts of this paper, the various techniques of segmentation are discussed and compared.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

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<span lang="EN-US">Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.</span>

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Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm

Cited by 39 publications

References 60 publications

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Brain Image Segmentation in Recent Years: A Narrative Review

Brain stroke computed tomography images analysis using image processing: A Review

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