TY - JOUR
T1 - Multi-objective noise estimator for the applications of de-noising and segmentation of MRI data
AU - Singh, Munendra
AU - Verma, Ashish
AU - Sharma, Neeraj
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/9
Y1 - 2018/9
N2 - The present study proposes the noise estimation of Magnetic Resonance Imaging (MRI) data using multi-objective particle swarm optimisation (MOPSO). This adaptive noise estimation is based on the maximisation of the multiple quality measures, which enable the algorithm to achieve de-noising along with enhancement in the image features. The paper proposes two filtering approaches to de-noise MRI data. In first, MOPSO based noise estimation is followed by non-local statistics based Kalman filter, whereas, in the second approach, MOPSO based noise estimation is followed by Linear Minimum Mean Square Error (LMMSE) filter. The impact of de-noising on segmentation of MRI data has also been studied, for this purpose enhanced fuzzy c-means algorithm has been applied on filtered MRI data. The de-noising and segmentation performance of MOPSO-non local Kalman filter and MOPSO-LMMSE filters has been evaluated and compared with Wavelet filter, Wiener filter, non-local mean filter, standard Kalman and standard LMMSE filter. The proposed noise estimation approach followed by filtering is giving better de-noising and segmentation results as compared to standard filters considered.
AB - The present study proposes the noise estimation of Magnetic Resonance Imaging (MRI) data using multi-objective particle swarm optimisation (MOPSO). This adaptive noise estimation is based on the maximisation of the multiple quality measures, which enable the algorithm to achieve de-noising along with enhancement in the image features. The paper proposes two filtering approaches to de-noise MRI data. In first, MOPSO based noise estimation is followed by non-local statistics based Kalman filter, whereas, in the second approach, MOPSO based noise estimation is followed by Linear Minimum Mean Square Error (LMMSE) filter. The impact of de-noising on segmentation of MRI data has also been studied, for this purpose enhanced fuzzy c-means algorithm has been applied on filtered MRI data. The de-noising and segmentation performance of MOPSO-non local Kalman filter and MOPSO-LMMSE filters has been evaluated and compared with Wavelet filter, Wiener filter, non-local mean filter, standard Kalman and standard LMMSE filter. The proposed noise estimation approach followed by filtering is giving better de-noising and segmentation results as compared to standard filters considered.
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U2 - 10.1016/j.bspc.2018.07.012
DO - 10.1016/j.bspc.2018.07.012
M3 - Article
AN - SCOPUS:85051389035
VL - 46
SP - 249
EP - 259
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
SN - 1746-8094
ER -