TY - JOUR
T1 - Bat optimization based neuron model of stochastic resonance for the enhancement of MR images
AU - Singh, Munendra
AU - Verma, Ashish
AU - Sharma, Neeraj
N1 - Publisher Copyright:
© 2017 Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
PY - 2017
Y1 - 2017
N2 - 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.
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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U2 - 10.1016/j.bbe.2016.10.006
DO - 10.1016/j.bbe.2016.10.006
M3 - Article
AN - SCOPUS:85012249325
SN - 0208-5216
VL - 37
SP - 124
EP - 134
JO - Biocybernetics and Biomedical Engineering
JF - Biocybernetics and Biomedical Engineering
IS - 1
ER -