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Publication Type : Conference Proceedings
Publisher : ICACC 2012
Source : Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012, Cochin, p.82-85 (2012)
ISBN : 9780769547237
Keywords : Accurate estimation, Area ratios, Background region, Distribution models, Feature sets, Gaussians, Graphic methods, Higher order statistics, Identification approach, Kurtosis, Magnetic Resonance Imaging, Medical imaging, MR images, Noise estimation, Noise levels, Noise models, Noisy image, Peak ratios, Rayleigh, Standard deviation
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : Yes
Year : 2012
Abstract : In this paper, we study a set of histogram and higher-order statistical (HOS) features for automatically identifying the presence of large background in the magnitude MR images. The robustness and discriminative power of each individual feature and combining feature sets are investigated using different MR images including brain, cardiac, breast, spine, stomach and noisy images corrupted by Rician noise with different standard deviations, σ={5,10,15,20,25,30,35}. The performances of the identification approaches are evaluated in terms of sensitivity, specificity, and accuracy. Experimental results obtained on 2544 MR images show that an approach based on the kurtosis and histogram peak ratio (HPR) features outperforms significantly as compared to that of other approaches reported in this work. The proposed approach can be used for selection of distribution model (Rayleigh or Gaussian) for accurate estimation of Rician noise level in MR images having large or little background regions. © 2012 IEEE.
Cite this Research Publication : N. V. Varghees, Manikandan, M. S., J. Gini, R., and Dr. Soman K. P., “A New Framework to Automatically Select Noise Model for Rician Noise Estimation in MR Images”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 82-85, 2012.