Duke University COPYRIGHT © DUKE UNIVERSITY 2012 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 , May, 2012 OCTNEWS.ORG Feature Of The Week 6/24/12 Vision and Image Processing Laboratory

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 Vision and Image Processing Laboratory 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)

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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.

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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 PCA Classic paradigm: Learn the dictionary directly from the noisy image

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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.

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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 h th pixel in the reference noiseless image, represents the h th pixel of the denoised image, is the total number of pixels, and is the maximum intensity value of

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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 = 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 = MSR = 13.26, CNR = 5.19, PSNR = MSR = 11.96, CNR = 4.72, PSNR = Result using the MSBTD method MSR = 15.41, CNR = 5.98, PSNR = [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).

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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 = 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 = MSR = 12.82, CNR = 5.62, PSNR = MSR = 12.08, CNR = 5.31, PSNR = Result using the MSBTD method MSR = 15.28, CNR = 6.45, PSNR = [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).

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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).

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory 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).

COPYRIGHT © DUKE UNIVERSITY 2012 Vision and Image Processing Laboratory Software display MATLAB based MSBTD software & dataset is freely available at 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

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