Please use this identifier to cite or link to this item:
|Title:||A hybrid model for rician noise reduction in MRI|
|Citation:||Proceedings - 2nd International Conference on Advanced Computing, Networking and Security, ADCONS 2013, 2013, Vol., , pp.56-61|
|Abstract:||Magnetic Resonance Images (MRI) are normally corrupted with random noise mainly arised from the patient's body and from the scanning apparatus. This paper describes a new technique to remove the homogeneous Rician noise in the magnitude magnetic resonance (MR) images. Linear minimum mean square error (LMMSE) estimator is a good choice to solve this inverse problem. In another way, denoising can be considered as a solution for L1 regularization problem of compressed sensing (CS). The Split Bregman iteration technique is effectively used in this stage in order to minimize the total variation (TV) functional. By combining these results in transform domain, the denoising is expected to be improved. Experiments show that the proposed algorithm outperforms other existing methods in the literature in terms of Peak Signal to Noise Ratio (PSNR). � 2013 IEEE.|
|Appears in Collections:||2. Conference Papers|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.