Abstract
This chapter proposed an adaptive wavelet thresholding method for enhancement and de-noising of retinal optical coherence tomography (OCT) image. Speckle noise degrades the OCT image and affects the disease diagnostic utility. OCT image enhancement is required for accurate analysis of inter and intra retinal layers. Enhancement is achieved through histogram mapping called Gaussianization transform. Further wavelet coefficients are modeled statistically to get the signal and noise information for finding the threshold value for weighing the wavelet coefficients. A Cauchy distribution is used to model the wavelet coefficients. An adaptive soft thresholding is used to estimate the true wavelet coefficients. Gaussianization transform widen the intensity range and enhances the OCT image and de-noising performances. Through different performance parameters, it is demonstrated that the proposed method outperforms the state-of-the-art methods. The proposed de-noising method has achieved 4.67% improvement in Peak Signal-to-Noise Ratio (PSNR), 2.61% in Structural Similarity (SSIM), 1.33% in Correlation coefficient (CoC) and 9.4% in Edge Preservation Index (EPI) parameters than the adaptive soft thresholding method, designed without statistical modeling.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sahu, S., Singh, H.V., Kumar, B. and Singh, A.K., Statistical Modeling and Gaussianization Procedure based de-speckling algorithm for Retinal OCT images, Journal of Ambient Intelligence and Humanized Computing (AIHC), an International Journal of Springer. DOI: https://doi.org/10.1007/s12652-018-0823-2
Zaki, F., Wang, Y., Yuan, X. and Liu, X., 2017, June. Adaptive Wavelet Thresholding for Optical Coherence Tomography Image Denoising. In Computational Optical Sensing and Imaging (pp. CTh4B-4). Optical Society of America.
Anantrasirichai, N., Nicholson, L., Morgan, J.E., Erchova, I., Mortlock, K., North, R.V., Albon, J. and Achim, A., 2014. Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography. Computerized Medical Imaging and Graphics, 38(6), pp.526-539.
Kim, J., Miller, D. T., Kim, E., Oh, S., Oh, J., & Milner, T. E. (2005). Optical Coherence Tomography Speckle Reduction by a Partially Spatially Coherent Source. Journal of Biomedical Optics, 10(6), 064034-064034.
Pircher, M., Go, E., Leitgeb, R., Fercher, A. F., & Hitzenberger, C. K. (2003). Speckle reduction in optical coherence tomography by frequency compounding. Journal of Biomedical Optics, 8(3), 565-569.
Iftimia, N., Bouma, B. E., & Tearney, G. J. (2003). Speckle reduction in optical coherence tomography by path length encoded angular compounding. Journal of Biomedical Optics, 8(2), 260-263.
Ghafaryasl, B., Baart, R., de Boer, J.F., Vermeer, K.A. and van Vliet, L.J., 2017, February. Automatic estimation of retinal nerve fiber bundle orientation in SD-OCT images using a structure-oriented smoothing filter. In Medical Imaging 2017: Image Processing (Vol. 10133, p. 101330C). International Society for Optics and Photonics.
Zhang, A., Xi, J., Sun, J. and Li, X., 2017. Pixel-based speckle adjustment for noise reduction in Fourier-domain OCT images. Biomedical optics express, 8(3), pp.1721-1730.
Tang, C., Cao, L., Chen, J. and Zheng, X., 2017. Speckle noise reduction for optical coherence tomography images via non-local weighted group low-rank representation. Laser Physics Letters, 14(5), p.056002.
Esmaeili, M., Dehnavi, A.M., Rabbani, H. and Hajizadeh, F., 2017. Speckle noise reduction in optical coherence tomography using two-dimensional curvelet-based dictionary learning. Journal of medical signals and sensors, 7(2), p.86.
Adabi, S., Rashedi, E., Conforto, S., Mehregan, D., Xu, Q. and Nasiriavanaki, M., 2017, February. Speckle reduction of OCT images using an adaptive cluster-based filtering. In Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI (Vol. 10053, p. 100532X). International Society for Optics and Photonics.
Kato, Y., Kuroki, N., Hirose, T. and Numa, M., 2016. Locally weighted averaging for denoising of medical tomographic images. Journal of Signal Processing, 20(4), pp.217-220.
Rajabi, H. and Zirak, A., 2016. Speckle noise reduction and motion artifact correction based on modified statistical parameters estimation in OCT images. Biomedical Physics & Engineering Express, 2(3), p.035012.
Duan, J., Lu, W., Tench, C., Gottlob, I., Proudlock, F., Samani, N.N. and Bai, L., 2016. Denoising optical coherence tomography using second order total generalized variation decomposition. Biomedical Signal Processing and Control, 24, pp.120-127.
Baghaie, A., D’souza, R.M. and Yu, Z., 2016. Application of independent component analysis techniques in speckle noise reduction of retinal OCT images. Optik-International Journal for Light and Electron Optics, 127(15), pp.5783-5791.
Kim, K.S., Park, H.J. and Kang, H.S., 2015. Enhanced optical coherence tomography imaging using a histogram-based denoising algorithm. Optical Engineering, 54(11), p.113110.
Thapa, D., Raahemifar, K. and Lakshminarayanan, V., 2015. Reduction of speckle noise from optical coherence tomography images using multi-frame weighted nuclear norm minimization method. Journal of Modern Optics, 62(21), pp.1856-1864.
Aum, J., Kim, J.H. and Jeong, J., 2015. Effective speckle noise suppression in optical coherence tomography images using nonlocal means denoising filter with double Gaussian anisotropic kernels. Applied Optics, 54(13), pp.D43-D50.
Duan, J., Tench, C., Gottlob, I., Proudlock, F. and Bai, L., 2015. New variational image decomposition model for simultaneously denoising and segmenting optical coherence tomography images. Physics in Medicine & Biology, 60(22), p.8901.
Avanaki, M.R., Marques, M.J., Bradu, A., Hojjatoleslami, A. and Podoleanu, A.G., 2014, March. A new algorithm for speckle reduction of optical coherence tomography images. In Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XVIII (Vol. 8934, p. 893437). International Society for Optics and Photonics.
Bian, L., Suo, J., Chen, F. and Dai, Q., 2015. Multiframe denoising of high-speed optical coherence tomography data using interframe and intraframe priors. Journal of biomedical optics, 20(3), p.036006.
Gyger, C., Cattin, R., Hasler, P.W. and Maloca, P., 2014. Three-dimensional speckle reduction in optical coherence tomography through structural guided filtering. Optical Engineering, 53(7), p.073105.
Cheng, J., Duan, L., Wong, D.W.K., Akiba, M. and Liu, J., 2014, August. Speckle reduction in optical coherence tomography by matrix completion using bilateral random projection. In Engineering in Medicine and Biology Society (EMBC), 2014 36th annual international conference of the IEEE (pp. 186-189). IEEE.
Thapa, D., Raahemifar, K. and Lakshminarayanan, V., 2014, August. A new efficient dictionary and its implementation on retinal images. In Digital Signal Processing (DSP), 2014 19th International Conference on (pp. 841-846). IEEE.
Chen, Q., de Sisternes, L., Leng, T. and Rubin, D.L., 2015. Application of improved homogeneity similarity-based denoising in optical coherence tomography retinal images. Journal of digital imaging, 28(3), pp.346-361.
Xu, J., Ou, H., Lam, E.Y., Chui, P.C. and Wong, K.K., 2013. Speckle reduction of retinal optical coherence tomography based on contourlet shrinkage. Optics letters, 38(15), pp.2900-2903.
Guo, Q., Dong, F., Sun, S., Lei, B. and Gao, B.Z., 2013. Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image. IET image processing, 7(5), pp.442-450.
Luan, F. and Wu, Y., 2013. Application of RPCA in optical coherence tomography for speckle noise reduction. Laser Physics Letters, 10(3), p.035603.
Cao, J., Wang, P., Wu, B., Shi, G., Zhang, Y., Li, X., Zhang, Y. and Liu, Y., 2018. Improved wavelet hierarchical threshold filter method for optical coherence tomography image de-noising. Journal of Innovative Optical Health Sciences, 11(03), p.1850012.
Sahu, S., Singh, H. V., Kumar, B., & Singh, A. K. (2017). De-noising of ultrasound image using Bayesian approached heavy-tailed Cauchy distribution. Multimedia Tools and Applications, 1-18.
Sahu, S., Singh, H. V., Kumar, B., & Singh, A. K. A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images. Journal of Intelligent Systems.
Bhuiyan, M. I. H., Ahmad, M. O., & Swamy, M. N. S. (2007). Spatially adaptive wavelet-based method using the Cauchy prior for denoising the SAR images. IEEE Transactions on Circuits and Systems for Video Technology, 17(4), 500-507.
Amini, Z., & Rabbani, H. (2016). Statistical modeling of retinal optical coherence tomography. IEEE transactions on medical imaging, 35(6), 1544-1554.
Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE transactions on information theory, 41(3), 613-627.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sahu, S., Singh, H.V., Kumar, B., Singh, A.K., Kumar, P. (2019). Enhancement and De-Noising of OCT Image by Adaptive Wavelet Thresholding Method. In: Singh, A., Mohan, A. (eds) Handbook of Multimedia Information Security: Techniques and Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-15887-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-15887-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15886-6
Online ISBN: 978-3-030-15887-3
eBook Packages: Computer ScienceComputer Science (R0)