Automated background segmentation for Rician noise estimation of noisy MR images

2012 ◽  
Author(s):  
Hoang Vinh Tran ◽  
Danchi Jiang
Keyword(s):  
Noise Estimation ◽  
Rician Noise ◽  
Mr Images ◽  
Background Segmentation

scholarly journals Object-based Rician Noise Estimation for MR Images

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S81
Author(s):  
P. Coupé ◽  
J.V. Manjón ◽  
E. Gedamu ◽  
D. Arnold ◽  
M. Robles ◽  
...  
Keyword(s):  
Noise Estimation ◽  
Rician Noise ◽  
Mr Images ◽  
Object Based

scholarly journals An Object-Based Method for Rician Noise Estimation in MR Images

2009 ◽  
pp. 601-608
Author(s):  
Pierrick Coupé ◽  
José V. Manjón ◽  
Elias Gedamu ◽  
Douglas Arnold ◽  
Montserrat Robles ◽  
...  
Keyword(s):  
Noise Estimation ◽  
Rician Noise ◽  
Mr Images ◽  
Object Based

scholarly journals Robust Rician noise estimation for MR images

2010 ◽  
Vol 14 (4) ◽  
pp. 483-493 ◽  
Author(s):  
Pierrick Coupé ◽  
José V. Manjón ◽  
Elias Gedamu ◽  
Douglas Arnold ◽  
Montserrat Robles ◽  
...  
Keyword(s):  
Noise Estimation ◽  
Rician Noise ◽  
Mr Images

scholarly journals Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2918 ◽  
Author(s):  
Houqiang Yu ◽  
Mingyue Ding ◽  
Xuming Zhang
Keyword(s):  
Similarity Index ◽  
Similarity Measures ◽  
Noise Removal ◽  
Human Vision ◽  
Rician Noise ◽  
Mr Images ◽  
Laplacian Eigenmaps ◽  
Nonlocal Means ◽  
Output Layer ◽  
Denoised Image

Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

Denoising of MR images with Rician noise using a wider neural network and noise range division

2019 ◽  
Vol 64 ◽  
pp. 154-159 ◽  
Author(s):  
Xuexiao You ◽  
Ning Cao ◽  
Hao Lu ◽  
Minghe Mao ◽  
Wei Wanga,
Keyword(s):  
Neural Network ◽  
Rician Noise ◽  
Mr Images

MRI Denoising using Sparse Based Curvelet Transform with Variance Stabilizing Transformation Framework

2017 ◽  
Vol 7 (1) ◽  
pp. 116 ◽  
Author(s):  
Sidheswar Routray ◽  
Arun Kumar Ray ◽  
Chandrabhanu Mishra
Keyword(s):  
Sparse Representation ◽  
Curvelet Transform ◽  
Noise Removal ◽  
Rician Noise ◽  
Mr Images ◽  
Denoising Method ◽  
Post Process ◽  
Inverse Variance ◽  
Improved Performance ◽  
Texture Preservation

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.

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