sUre-let approach for Mr Brain Image Denoising Using Different shrinkage rules

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have significant impact on the shapes and orientations of tensors in diffusion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error—Stein’s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform. DOI: 10.4018/jhisi.2010040108 74 International Journal of Healthcare Information Systems and Informatics, 5(2), 73-81, April-June 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. for image denoising in the wavelet domain based on the generalized Gaussian distribution (GGD) modeling of subband coefficients. Wang and Zhou (n.d.) proposed a denoising algorithm for medical images based on a combination of the total variation minimization scheme and the wavelet scheme. Wavelet domain filtering is applied to an MRI images in removing noise (Nowak, 1999; Nowak, Gregg, Coopery, & Sieberty, n.d.). In Starck, Candes, and Donoho (2002), Curvelet transform and Ridgelet transform are used to remove noise from the natural image. In Zhang, Fadili, and Starck (2008), the author proposed a method to remove the poission noise in natural images using wavelet, Ridgelet and Curvelet. The superiority of Curvelet transform in medical image denoising is proved in Parthiban and Subramanian (2006). Luisier and Blu proposed SURE Approach for removing Gaussian noise in natural images (Blu & Luisier, 2007; Luisier, Blu, & Unser, 2007). In this work, the SURE-LET Approach is used for removing Rician noise in MRI brain images. For comparative study, Curvelet transform also used for same purpose. The paper is organized as follows. First, in Section II, the Noise in MRI is dealt. Proposed Methodology is discussed in Section III, the SURE-LET Approach is described in Section IV and Curvelet Transform for denoising is described in section V. Results and Discussion are dealt in section VI. Finally, conclusions are given in Section VII.