Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm

Oliva, Diego; Hinojosa, Salvador; Cuevas, Erik; Pajares, Gonzalo; Ávalos, Omar; Glvez, Jorge

doi:10.1016/j.eswa.2017.02.042

Cited by 174 publications

(58 citation statements)

References 39 publications

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“…3 The medical brain image provides useful and detailed data with respect to normal and abnormal brain tissues. treatment choices are basically chosen by the components, for example, the degree to which the tumor has spread to alternate parts of the Central Nervous System (CNS), the conceivable reactions on the patient identifying with the treatment strategy, and the general strength of the patient.…”

Section: Ofmentioning

confidence: 99%

“…treatment choices are basically chosen by the components, for example, the degree to which the tumor has spread to alternate parts of the Central Nervous System (CNS), the conceivable reactions on the patient identifying with the treatment strategy, and the general strength of the patient. 3 The medical brain image provides useful and detailed data with respect to normal and abnormal brain tissues. Presently, MR pictures are the most widely recognized test for diagnosing and affirming the nearness of brain cancer.…”

Section: Ofmentioning

confidence: 99%

“…The reasons are because of variety of conceivable complex areas, shapes, size, and picture intensity for various kinds of brain cancers. 3 Radiologists analyze the brain MRI cuts via visual screening to distinguish and identifying the presence of tumor or unusual tissue. The brain diagnoses depend on the region, shape, and pictures intensities of various sorts of brain cancers.…”

Section: Ofmentioning

confidence: 99%

See 2 more Smart Citations

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Arunkumar

Mohammed

Mostafa

et al. 2018

Concurrency and Computation

130

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The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues using MR images. The method includes three computer vision fiction strategies which are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOG features. A fully automatic model-based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI. However, identification the tumor kind utilizing the texture features. A total of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model-based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%, with 89 sensitivity and 94 specificity.

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Section: Ofmentioning

confidence: 99%

Section: Ofmentioning

confidence: 99%

Section: Ofmentioning

confidence: 99%

See 1 more Smart Citation

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Arunkumar

Mohammed

Mostafa

et al. 2018

Concurrency and Computation

130

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“…Different from the traditional PSO algorithm, the CSA algorithm introduces an anti‐tracking mechanism, which significantly reduces the probability of falling into local optimum when dealing with multipeak problems . The proposed CSA has been applied to optimization problems in many different fields, such as the image segmentation and diagnosis of Parkinson's disease, which have achieved satisfactory optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Finite element model updating using crow search algorithm with Levy flight

et al. 2020

Numerical Meth Engineering

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Summary The accuracy of the finite element model is essential for fatigue analysis and vibration of a flexible body. This paper presents a finite element model updating (FEMU) algorithm based on crows search algorithm incorporated with Levy flight (LFCSA). The proposed algorithm is tested on the updating of two different cases: a simple structure (beam) and a complex structure (gearbox housing). To verify the performance of LFCSA, two optimization algorithm, which is the standard CSA and the particle swarm optimization algorithm with Levy flight (LFPSO), are applied to FEMU of the simple beam. The result of the comparison shows that the LFCSA has sufficient convergence speed and global search ability in the implementation of FEMU. In the case of gearbox housing, the error of frequencies and mode shapes are analyzed to indicate that the LFCSA gives a satisfactory result in a complex structure.

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“…Its published results demonstrate its capacity to solve several complex engineering optimization problems. Some examples include its application to image processing [34] and water resources [35]. In spite of its interesting results, its search strategy presents great difficulties when it faces high multi-modal formulations.…”

Section: Introductionmentioning

confidence: 99%

An Improved Crow Search Algorithm Applied to Energy Problems

et al. 2018

Self Cite

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Abstract:The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA) is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I) the awareness probability (AP) and (II) the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.

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Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm

Cited by 174 publications

References 39 publications

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Finite element model updating using crow search algorithm with Levy flight

An Improved Crow Search Algorithm Applied to Energy Problems

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