MRI Denoising using Sparse Based Curvelet Transform with Variance Stabilizing Transformation Framework
We develop an efficient MRI denoising algorithm based on sparse representation and curvelet transform with variance stabilizing transformation framework. By using sparse representation, a MR image is decomposed into a sparsest coefficients matrix with more no of zeros. Curvelet transform is directio...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2017-07-01.
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| Online Access: | Get fulltext |
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| LEADER | 02198 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs6649_7316 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Routray, Sidheswar |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Ray, Arun Kumar |e author |
| 700 | 1 | 0 | |a Mishra, Chandrabhanu |e author |
| 245 | 0 | 0 | |a MRI Denoising using Sparse Based Curvelet Transform with Variance Stabilizing Transformation Framework |
| 260 | |b Institute of Advanced Engineering and Science, |c 2017-07-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6649 | ||
| 520 | |a We develop an efficient MRI denoising algorithm based on sparse representation and curvelet transform with variance stabilizing transformation framework. By using sparse representation, a MR image is decomposed into a sparsest coefficients matrix with more no of zeros. Curvelet transform is directional in nature and it preserves the important edge and texture details of MR images. In order to get sparsity and texture preservation, we post process the denoising result of sparse based method through curvelet transform. To use our proposed sparse based curvelet transform denoising method to remove rician noise in MR images, we use forward and inverse variance-stabilizing transformations. Experimental results reveal the efficacy of our approach to rician noise removal while well preserving the image details. Our proposed method shows improved performance over the existing denoising methods in terms of PSNR and SSIM for T1, T2 weighted MR images. | ||
| 540 | |a Copyright (c) 2017 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 7, No 1: July 2017; 116-122 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v7.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6649/7316 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6649/7316 |z Get fulltext |