Denoising Medical Images Using Hybrid Filter With Firefly Algorithm

Sam, B. Baron; Fred, A. Lenin

doi:10.1109/icraecc43874.2019.8995015

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Comparison summary of proposed denoising methods with different denoising methods. 44 PEM parameters Yadav et al 14 Parmar et al 17 Sam and Fred 18 Kumar et al 20…”

Section: Discussionmentioning

confidence: 99%

“…Parmar et al 17 have proposed an efficient approach based on non-local mean filter properties, which can be used for image denoising due to the sparsity and multiresolution structure of non-local mean filter properties. Sam and Fred 18 have utilized a blend of filters to assess the noise from the images. The filtered output images are then evaluated for exact image information using the method of firefly algorithm in light of artificial neural network systems.…”

Section: Introductionmentioning

confidence: 99%

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Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images

Kumar

Srivastava

Sai³

2021

Journal of Defense Modeling & Simulation

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The evolution of magnetic resonance imaging (MRI) leads to the study of the internal anatomy of the breast. It maps the physical features along with functional characteristics of selected regions. However, its mapping accuracy is affected by the presence of Rician noise. This noise limits the qualitative and quantitative measures of breast image. This paper proposes recasted nonlinear complex diffusion filter for sharpening the details and removal of Rician noise. It follows maximum likelihood estimation along with optimal parameter selection of complex diffusion where the overall functionality is balanced by regularization parameters. To make recasted nonlinear complex diffusion, the edge threshold constraint “k” of diffusion coefficient is reformed. It is replaced by the standard deviation of the image. It offers a wide range of threshold as variability present in the image with respect to edge. It also provides an automatic selection of “k” instead of user-based value. A series of evaluation has been conducted with respect to different noise ratios further quality improvement of MRI. The qualitative and quantitative assessments of evaluations are tested for the Reference Image Database to Evaluate Therapy Response (RIDER) Breast database. The proposed method is also compared with other existing methods. The quantitative assessment includes the parameters of the full-reference image, human visual system, and no-reference image. It is observed that the proposed method is capable of preserving edges, sharpening the details, and removal of Rician noise.

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“…Comparison summary of proposed denoising methods with different denoising methods. 44 PEM parameters Yadav et al 14 Parmar et al 17 Sam and Fred 18 Kumar et al 20…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images

Kumar

Srivastava

Sai³

2021

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

show abstract

“…This produced noise appears due to transmission, poor enlightenment, and camera sensor cells respectively. So, Sam and FRED [111] have used a merge of filters to review the images through the noise. The considered Metaheuristics were used by the authors to train FFNN.…”

Section: Fa In Medical Image Pre-processingmentioning

confidence: 99%

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Nayak

Naik

Dinesh³

et al. 2020

SN COMPUT. SCI.

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Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

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Blood Cell Image Denoising Based on Tunicate Rat Swarm Optimization with Median Filter

Dhas¹,

Singh²

2022

Evolutionary Computing and Mobile Sustainable Networks

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Denoising Medical Images Using Hybrid Filter With Firefly Algorithm

Cited by 5 publications

References 8 publications

Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images

Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Blood Cell Image Denoising Based on Tunicate Rat Swarm Optimization with Median Filter

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