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Project Meeting
Sparsity Based Denoising of Spectral Domain Optical Coherence Tomography Images
Leyuan Fang, Shutao Li, Qing Nie, Joseph A. Izatt, Cynthia A. Toth, and Sina Farsiu
Biomedical Optics Express, 3(5), pp. 927-942, May, 2012
OCTNEWS.ORG Feature Of The Week 6/24/12
Leyuan.Fang@duke.edu
Vision and Image ProcessingLaboratory
Duke University
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Content
1. Introduction
2. Multiscale structural dictionary
3. Non-local denoising
4. Results comparison
5. Software display
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Introduction
Two classic denoising frameworks:
1. multi-frame averaging technique
Low quality denoising result
High quality denoising result but requires higher image acquisition time
2. model-based single-frame techniques (e.g. Wiener filtering, kernel regression, or wavelets)
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Proposed Method Overview
We introduce the Multiscale Sparsity Based Tomographic Denoising (MSBTD) framework.
MSBTD is a hybrid more efficient alternative to the noted two classic denoising frameworks applicable to virtually all tomographic imaging modalities.
MSBTD utilizes a non-uniform scanning pattern, in which, a fraction of B-scans are captured slowly at a relatively higher than nominal SNR.
The rest of the B-scans are captured fast at the nominal SNR.
Utilizing the compressive sensing principles, we learn a sparse representation dictionary for each of these high-SNR images and utilize these dictionaries to denoise the neighboring low-SNR B-scans.
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Assumption
In common SDOCT volumes, neighboring B-scans have similar texture and noise pattern.
summed-voxel projection (SVP) en face SDOCT image
B-Scan acquired from the location of the blue line
B-Scan acquired from the location of the yellow line
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Sparse Representation
SDOCT image or its patches
Dictionary to represent the SDOCT image
Sparse coefficients
Our paradigm:
Learn the dictionary from the neighboring high-SNR B-scan
How to learn the dictionary?
Train by K-SVD
Train by K-SVD
Train by PCA
Classic paradigm:
Learn the dictionary directly from the noisy image
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Multiscale structural dictionary
To better capture the properties of structures and textures of different size, we utilize a novel multi-scale variation of the structural dictionary representation.
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Non-local strategy
To further improve the performance, we search for the similar patches in the SDOCT images and average them to achieve better results.
The MSTBD denoising process
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Results comparison
Quantitative measures
1. Mean-to-standard-deviation ratio (MSR)
where and are the mean and the standard deviation of the foreground regions
2. Contrast-to-noise ratio (CNR)
where and are the mean and the standard deviation of the background regions
3. Peak signal-to-noise-ratio (PSNR)
where is the hth pixel in the reference noiseless image , represents the hth pixel
of the denoised image , is the total number of pixels, and is the maximum
intensity value of
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Results comparison
Experiment 1: denoising (on normal subject image) based on learned dictionary from a nearby high-SNR Scan
Averaged image Noisy image (Normal subject) Result using the Tikhonov method [1]
MSR = 10.64, CNR = 3.90 MSR = 3.20, CNR = 1.17 MSR = 7.65, CNR = 3.25, PSNR = 23.35
Result using the NEWSURE method [2] Result using the KSVD method [3] Result using the BM3D method [4]
MSR = 7.85, CNR = 2.87, PSNR = 24.51 MSR = 13.26, CNR = 5.19, PSNR = 28.48 MSR = 11.96, CNR = 4.72, PSNR = 28.35
Result using the MSBTD method
MSR = 15.41, CNR = 5.98, PSNR = 28.83
[1] G. T. Chong, et al., Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography, Arch. Ophthalmol. (2009).
[2] F. Luisier, et al., A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding, IEEE Trans. Image Process (2007).
[3] M. Elad, et al., Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process. (2006).
[4] K. Dabov, et al., Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. (2007).
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Results comparison
Experiment 1: denoising (on dry AMD subject image) based on learned dictionary from a nearby high-SNR Scan
Averaged image Noisy image (AMD subject) Result using the Tikhonov method [1]
MSR = 10.20, CNR = 3.75 MSR = 3.46, CNR = 1.42 MSR = 8.12, CNR = 3.92, PSNR = 21.76
Result using the NEWSURE method [2] Result using the KSVD method [3] Result using the BM3D method [4]
MSR = 8.04, CNR = 3.39, PSNR = 23.87 MSR = 12.82, CNR = 5.62, PSNR = 26.07 MSR = 12.08, CNR = 5.31, PSNR = 25.68
Result using the MSBTD method
MSR = 15.28, CNR = 6.45, PSNR = 26.11
[1] G. T. Chong, et al., Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography, Arch. Ophthalmol. (2009).
[2] F. Luisier, et al., A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding, IEEE Trans. Image Process (2007).
[3] M. Elad, et al., Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process. (2006).
[4] K. Dabov, et al., Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. (2007).
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Results comparison
Experiment 2: denoising based on learned dictionary from a distant high-SNR scan
Summed-voxel projection (SVP) en face image Noisy image acquired from the location b
MSR = 3.10, CNR = 1.01
Result using the KSVD method [1] Result using the BM3D method [2] Result using the MSBTD method
MSR = 13.93, CNR = 5.03 MSR = 14.93, CNR = 5.46 MSR = 18.57, CNR = 6.88
[1] M. Elad, et al., Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process. (2006).
[2] K. Dabov, et al., Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. (2007).
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Results comparison
Experiment 2: denoising based on learned dictionary from a distant high-SNR scan
Summed-voxel projection (SVP) en face image Noisy image acquired from the location c
MSR = 3.30, CNR = 1.40
Result using the KSVD method [1] Result using the BM3D method [2] Result using the MSBTD method
MSR = 10.30, CNR = 4.95 MSR = 9.91, CNR = 4.70 MSR = 11.71, CNR = 5.35
[1] M. Elad, et al., Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process. (2006).
[2] K. Dabov, et al., Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. (2007).
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Software display
MATLAB based MSBTD software & dataset is freely available at
http://www.duke.edu/~sf59/Fang_BOE_2012.htm
Input the test image
Input the
Averaged image
Setting the parameters for the MSBTD
Run the MSBTD
Run the Tikhonov
Save the results
Select region from the test image
Select a background region from the test image
Select a foregournd region from the test image
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CLICK ON THE GUI TO PLAY VIDEO DEMO OF THE SOFTWARE
MATLAB based MSBTD software & dataset is freely available at
http://www.duke.edu/~sf59/Fang_BOE_2012.htm
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Training image
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Patches extracted from
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Downsamped image
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Upsampled Image
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Clustering
Structural clusters
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Structural
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Multiscale structural
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Dictionary
selection
Similar patches
searching
Sparse
representation
Sparse representation
with the selected
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Reconstruction
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