Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm

Zhou, Yuanping; Shi, Changqin; Lai, Bingyan; Jimenez, Giorgos

doi:10.21037/qims.2019.08.19

Cited by 57 publications

(27 citation statements)

References 49 publications

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“…The proposed method uses 8-bit gray-level images with 8 layers (see Figures 3 and 4 ). The images were selected and proposed from the existing state-of-the-art methods [ 58 – 61 ]. These images were processed to 8-bit layers since HNN is a bipolar system, allowing only input data with −1 and +1.…”

Section: Methodsmentioning

confidence: 99%

“…Higher WPSNR values indicate higher quality of the enhanced image Contrast: it is a statistical measure which results in the difference in the value between image intensity and its neighbor for the input image [ 58 ]. Higher contrast values are desirable as it indicate better visual appearance Homogeneity: it is a measure of likeliness of the image intensities [ 58 ]. This measure suggests that higher values of homogeneity are desirable and indicate higher quality image …”

Section: Methodsmentioning

confidence: 99%

“…Contrast: it is a statistical measure which results in the difference in the value between image intensity and its neighbor for the input image [ 58 ]. Higher contrast values are desirable as it indicate better visual appearance…”

Section: Methodsmentioning

confidence: 99%

“…Homogeneity: it is a measure of likeliness of the image intensities [ 58 ]. This measure suggests that higher values of homogeneity are desirable and indicate higher quality image…”

Section: Methodsmentioning

confidence: 99%

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Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Alenezi

Santosh

2021

International Journal of Biomedical Imaging

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One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Homogeneity: it is a measure of likeliness of the image intensities [ 58 ]. This measure suggests that higher values of homogeneity are desirable and indicate higher quality image…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Alenezi

Santosh

2021

International Journal of Biomedical Imaging

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“…As it can be observed, the larger values obtained when QSSA-IE is used over the other image enhancement algorithms. Despite mean intensity, PSNR and MSE value are quantitative measurements of the image quality; they cannot be the same as human eye [28]. This is due to the sensitivity to the errors isn't absolute.…”

Section: Qssa For Contrast Enhancement Problemmentioning

confidence: 99%

Hybrid Quantum Salp Swarm Algorithm for Contrast Enhancement of Natural Images

Sayed¹,

Khoriba²,

Haggag³

2019

IJIES

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This paper proposes a new hybrid approach based on quantum computing and SSA to boost the performance of SSA through finding the optimal trade-off between exploitation and exploration. QSSA relies on embedding the integration of the quantum operators and interference in the optimization process of SSA and the quantum representation of the search space. QSSA is tested on 29 global benchmark functions of CEC2017 and 8 benchmark images. The simulation results showed that employing the principles of quantum can significantly boost the performance of SSA. The performance of QSSA is compared with SSA and other recent and well-known optimization algorithms. The results on CEC2017 demonstrated the capability of QSSA to find the global optima for most of unimodal and multimodal benchmark functions, especially with complex search space. Moreover, the results revealed that the proposed image enhancement based QSSA is robust and can distinctly enhance the contrast of the color images.

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Optimum parameters extraction of proton exchange membrane fuel cells using Fractional‐Order Whale Optimization Algorithm

Ren

Eslami

2022

Energy Science & Engineering

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This study proposes a novel metaheuristic-based technique for optimum parameters' estimation of the proton exchange membrane fuel cells (PEMFCs).To provide better results with more reliability and accuracy, a Fractional Order-based design of the Whale Optimizer Algorithm (FO-WOA) is designed.A validation test showed that the proposed method provides a good trade-off between accuracy and convergence speed. Performing the algorithm for multiple independent runs also shows that the proposed method delivers reliable results toward some other comparative metaheuristic algorithms. This algorithm is then used for the minimization of the sum of square deviation between the experimental voltage-current polarization and the optimal achieved results by the model based on FO-WOA. The method is validated by considering two practical case studies, which are the Nexa PEMFC and 250 W PEM system, and its achievements are put in comparison with some approaches to indicate the higher effectiveness of the proposed method toward the others. The experimental results on the Nexa PEMFC indicate that the proposed FO-WOA-based method with 12 sum square errors (SSE) provided the minimum error toward the other. Also, the experiments on the 250 W PEM Stack indicated that for 250 W PEM Stack with 3/5 bar and 80°C, the proposed method with 0.01 SSE provides the fittest profile with the experimental data, and finally, the experiments on the same stack with 3/5 bar and 80°C showed the higher accuracy of the proposed method with the least SSE value (0.16) toward the others.

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Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm

Cited by 57 publications

References 49 publications

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Hybrid Quantum Salp Swarm Algorithm for Contrast Enhancement of Natural Images

Optimum parameters extraction of proton exchange membrane fuel cells using Fractional‐Order Whale Optimization Algorithm

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