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The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

D. Selvathi, S. Thamarai Selvi, C. Loorthu Sahaya Malar
Copyright: © 2010 |Volume: 5 |Issue: 2 |Pages: 9
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781609603960|DOI: 10.4018/jhisi.2010040108
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MLA

Selvathi, D., et al. "The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules." IJHISI vol.5, no.2 2010: pp.73-81. http://doi.org/10.4018/jhisi.2010040108

APA

Selvathi, D., Selvi, S. T., & Malar, C. L. (2010). The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules. International Journal of Healthcare Information Systems and Informatics (IJHISI), 5(2), 73-81. http://doi.org/10.4018/jhisi.2010040108

Chicago

Selvathi, D., S. Thamarai Selvi, and C. Loorthu Sahaya Malar. "The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules," International Journal of Healthcare Information Systems and Informatics (IJHISI) 5, no.2: 73-81. http://doi.org/10.4018/jhisi.2010040108

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Abstract

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 signi?cant impact on the shapes and orientations of tensors in di?usion 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.

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