New Improved Optimized Method for Medical Image Enhancement Based on Modified Shark Smell Optimization Algorithm

Zhou, Yuebin; Ye, Jianmin; Du, Yujin; Sheykhahmad, Fatima Rashid

doi:10.1007/s11220-020-00283-6

Cited by 20 publications

(11 citation statements)

References 62 publications

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“…The algorithm achieves great contrast enhancement but ignores medical image noise. 5 In 2020, Julio et al proposed a medical image enhancement method using multiscale top-hat transform by reconstructing multilevel features. The method improves contrast and detail, but performs poor generalization ability on some images with abundant details.…”

Section: Spatial Enhancement Methodsmentioning

confidence: 99%

“…Many scholars have conducted extensive research in this field. [1][2][3][4][5][6][7][8][9][10] This paper reviews mainstream image enhancement methods, including traditional histogram stretch methods, [11][12][13][14] image domain transformed methods, 15,16 image enhancement methods based on joint framework, and so on. Traditional image enhancement methods are mainly divided into spatial enhancement and frequency enhancement, which are used to enhance simple binary image.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, lung CT image enhancement acting as a preprocessing procedure can increase the accuracy and efficiency in actual medical applications, and has important clinical application value. Many scholars have conducted extensive research in this field 1–10 …”

Section: Introductionmentioning

confidence: 99%

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Lung CT image enhancement based on total variational frame and wavelet transform

Wang

Lei

et al. 2022

Int J Imaging Syst Tech

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Benefitting from the development of computer vision, computed tomography (CT) images have been used for assisting doctor's clinical diagnosis and improving the diagnostic efficiency. However, there exist some issues in medical images, such as low contrast, obscure detail, and complex noise due to the restriction of the system and the equipment in the process of imaging, medical images. To resolve these issues, a novel lung CT image enhancement method based on total variational framework combined with wavelet transform is proposed. Firstly, low‐frequency structure layer with low contrast and high‐frequency details layer with complex noise signals are acquired by decomposing the original image using total variational framework. Then, through the analysis of the histogram distribution characteristics of CT image, structure layer requires contrast enhancement; at the same time, the detail layer performs wavelet transform adaptive threshold denoising to remove noise. Finally, weight fusion of processed structure layer and details layer is performed to obtain the final fusion enhancement CT images. Experimental results show that the proposed method can enhance the contrast of clinical lung CT images, improve the clarity of details, and effectively suppress artifacts and complex noises. Contrast and sharpness—objective indicators—prove the proposed method's advantages. Subjectively, the proposed method performs superior over other existing CT enhancement methods, which achieves a better visual recognition.

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Section: Spatial Enhancement Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lung CT image enhancement based on total variational frame and wavelet transform

Wang

Lei

et al. 2022

Int J Imaging Syst Tech

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“…SOA is an effective bioinspired optimization algorithm [ 23 ]. It is commonly employed in different situations [ 24 , 25 , 26 ], such as cloud job scheduling, resolving arithmetical functions, training ANNs, constructing load forecast, healthcare image development, and optimum process of the reservoir. The SOA was stimulated by the shark behaviors.…”

Section: The Proposed Modelmentioning

confidence: 99%

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

Ragab

Eljaaly

Alhakamy

et al. 2021

Biology

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Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.

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“…Image enhancement is a method of improving the content of image for specific application. Medical image enhancement is one of categories of image processing which has a big effect on the diagnosis decisions ( Zhou et al, 2020 ). One of important topic in medical imaging is the “image contrast” which is related to the brightest and the darkest pixels inside the image.…”

Section: Introductionmentioning

confidence: 99%

Mathematical model based on fractional trace operator for COVID-19 image enhancement

Karim

Jalab

Ibrahim³

et al. 2022

Journal of King Saud University - Science

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New Improved Optimized Method for Medical Image Enhancement Based on Modified Shark Smell Optimization Algorithm

Cited by 20 publications

References 62 publications

Lung CT image enhancement based on total variational frame and wavelet transform

Lung CT image enhancement based on total variational frame and wavelet transform

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

Mathematical model based on fractional trace operator for COVID-19 image enhancement

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