Vision and Image Processing Laboratory
Sparsity Based Denoising of Spectral Domain
Optical
Coherence Tomography Images
Duke University
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
COPYRIGHT © DUKE UNIVERSITY 2012
Vision and Image
Processing Laboratory
Content
1. Introduction
2. Multiscale structural dictionary
3. Non-local denoising
4. Results comparison
5. Software display
COPYRIGHT © DUKE UNIVERSITY 2012
Introduction
Vision and Image
Processing Laboratory
Two classic denoising frameworks:
1. multi-frame averaging technique
2. model-based single-frame techniques (e.g.
Wiener filtering, kernel regression, or wavelets)
Low quality
denoising result
High quality denoising result but
requires higher image acquisition time
COPYRIGHT © DUKE UNIVERSITY 2012
Proposed Method Overview
Vision and Image
Processing Laboratory
• 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 Bscans.
COPYRIGHT © DUKE UNIVERSITY 2012
Assumption
Vision and Image
Processing Laboratory
In common SDOCT volumes, neighboring B-scans have
similar texture and noise pattern.
B-Scan acquired from the location of
the blue line
summed-voxel projection (SVP) en
face SDOCT image
B-Scan acquired from the location of
the yellow line
COPYRIGHT © DUKE UNIVERSITY 2012
Sparse Representation
Vision and Image
Processing Laboratory
y  Da
Sparse coefficients
How to learn
the dictionary?
SDOCT image or its patches Dictionary to represent the
SDOCT image
Classic paradigm:
Learn the dictionary directly from
the noisy image
Train by
K-SVD
Our paradigm:
Learn the dictionary from the
neighboring high-SNR B-scan
Train by K-SVD
COPYRIGHT © DUKE UNIVERSITY 2012
Train by PCA
Multiscale structural dictionary
Vision and Image
Processing Laboratory
To better capture the properties of structures and textures
of different size, we utilize a novel multi-scale variation of
the structural dictionary representation.
Upsampled Image
(Finer scale)
Upsampled and
downsampled
Structural
dictionary
learning
Clustering
Training image
Downsamped image
(Coarser scale)
Patches extracted from
images at different scales
Structural clusters
at different scales
COPYRIGHT © DUKE UNIVERSITY 2012
Non-local strategy
Vision and Image
Processing Laboratory
To further improve the performance, we search for the similar patches in the
SDOCT images and average them to achieve better results.
Multiscale structural
dictionary learning
Similar patches
searching
Dictionary
selection
Sparse
representation Sparse representation Reconstruction
with the selected
dictionary
The MSTBD denoising process
COPYRIGHT © DUKE UNIVERSITY 2012
Results comparison
Vision and Image
Processing Laboratory
Quantitative measures
1. Mean-to-standard-deviation ratio (MSR)
f
MSR =
,
where
f
f
and
 f are the mean and the standard deviation of the foreground regions
2. Contrast-to-noise ratio (CNR)
CNR 
where
|  f  b |
0.5(   )
2
f
2
b
,
b and  b are the mean and the standard deviation of the background regions
3. Peak signal-to-noise-ratio (PSNR)


PSNR = 20  log10 
 1

 H
Max R

H
h 1
ˆ
Rh  R
h

2


,



ˆ represents the hth pixel
where R h is the hth pixel in the reference noiseless image R , R
h
of the denoised image Rˆ , H is the total number of pixels, and Max R is the maximum
intensity value of R
COPYRIGHT © DUKE UNIVERSITY 2012
Results comparison
Vision and Image
Processing Laboratory
Experiment 1: denoising (on normal subject image) based on
learned dictionary from a nearby high-SNR Scan
Averaged image
MSR = 10.64, CNR = 3.90
Noisy image (Normal subject)
MSR = 3.20, CNR = 1.17
Result using the NEWSURE method [2] Result using the KSVD method [3]
MSR = 7.85, CNR = 2.87, PSNR = 24.51
Result using the Tikhonov method [1]
MSR = 7.65, CNR = 3.25, PSNR = 23.35
Result using the BM3D method [4]
MSR = 13.26, CNR = 5.19, PSNR = 28.48
MSR = 11.96, CNR = 4.72, PSNR = 28.35
[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
Result using the MSBTD method
wavelet thresholding,” IEEE Trans. Image
MSR
= 15.41, CNR = 5.98, PSNR = 28.83
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).
COPYRIGHT © DUKE UNIVERSITY 2012
Results comparison
Vision and Image
Processing Laboratory
Experiment 1: denoising (on dry AMD subject image) based on
learned dictionary from a nearby high-SNR Scan
Averaged image
MSR = 10.20, CNR = 3.75
Noisy image (AMD subject)
MSR = 3.46, CNR = 1.42
Result using the NEWSURE method [2] Result using the KSVD method [3]
MSR = 8.04, CNR = 3.39, PSNR = 23.87
MSR = 12.82, CNR = 5.62, PSNR = 26.07
[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
Result using the MSBTD method
wavelet thresholding,” IEEE Trans. Image
MSR
= 15.28, CNR = 6.45, PSNR = 26.11
Process (2007).
COPYRIGHT © DUKE UNIVERSITY 2012
Result using the Tikhonov method [1]
MSR = 8.12, CNR = 3.92, PSNR = 21.76
Result using the BM3D method [4]
MSR = 12.08, CNR = 5.31, PSNR = 25.68
[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).
Results comparison
Vision and Image
Processing Laboratory
Experiment 2: denoising based on learned dictionary from a
distant high-SNR scan
Summed-voxel projection (SVP) en face image
Result using the KSVD method [1]
MSR = 13.93, CNR = 5.03
Noisy image acquired from the location b
MSR = 3.10, CNR = 1.01
Result using the BM3D method [2]
MSR = 14.93, CNR = 5.46
Result using the MSBTD method
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).
COPYRIGHT © DUKE UNIVERSITY 2012
Results comparison
Vision and Image
Processing Laboratory
Experiment 2: denoising based on learned dictionary from a
distant high-SNR scan
Summed-voxel projection (SVP) en face image
Result using the KSVD method [1]
MSR = 10.30, CNR = 4.95
Noisy image acquired from the location c
MSR = 3.30, CNR = 1.40
Result using the BM3D method [2]
MSR = 9.91, CNR = 4.70
Result using the MSBTD method
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).
COPYRIGHT © DUKE UNIVERSITY 2012
Software display
Vision and Image
Processing Laboratory
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 Tikhonov
Save the results
Run the MSBTD
Select region from
the test image
Select a background
region from the test image
Select a foregournd region
from the test image
COPYRIGHT © DUKE UNIVERSITY 2012
Vision and Image
Processing Laboratory
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
COPYRIGHT © DUKE UNIVERSITY 2012