Sparsity Based Denoising of Spectral Domain Optical Coherence Tomography Images

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

[email protected]

Vision and Image ProcessingLaboratory

Duke University

COPYRIGHT DUKE UNIVERSITY 2012



Content

1. Introduction

2. Multiscale structural dictionary

3. Non-local denoising

4. Results comparison

5. Software display



2

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)



3

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.



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



5

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



6

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.



7

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



8

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



9

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).



10

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).





11

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





12

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





13

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



14

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



15

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Training image

Upsampled and

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Patches extracted from

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Downsamped image

(Coarser scale)

Upsampled Image

(Finer scale)

Clustering

Structural clusters

at different scales

Structural

dictionary

learning

Multiscale structural

dictionary learning

Dictionary

selection

Similar patches

searching

Sparse

representation

Sparse representation

with the selected

dictionary

Reconstruction

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Sparsity Based Denoising of Spectral Domain Optical Coherence Tomography Images

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