Bat optimization based neuron model of stochastic resonance for the enhancement of MR images

Munendra Singh; Ashish Verma; Neeraj Sharma

doi:10.1016/j.bbe.2016.10.006

Bat optimization based neuron model of stochastic resonance for the enhancement of MR images

Munendra Singh^*
, Ashish Verma
, Neeraj Sharma

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

44 Citations (Scopus)

Abstract

Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.

Original language	English
Pages (from-to)	124-134
Number of pages	11
Journal	Biocybernetics and Biomedical Engineering
Volume	37
Issue number	1
DOIs	https://doi.org/10.1016/j.bbe.2016.10.006
Publication status	Published - 2017

All Science Journal Classification (ASJC) codes

Biomedical Engineering

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10.1016/j.bbe.2016.10.006

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title = "Bat optimization based neuron model of stochastic resonance for the enhancement of MR images",

abstract = "Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.",

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AU - Singh, Munendra

AU - Verma, Ashish

AU - Sharma, Neeraj

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AB - Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.

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Bat optimization based neuron model of stochastic resonance for the enhancement of MR images

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