We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

A Study on Speckle Noise Removal and Segmentation of Retinal Layers in OCT Image Analysis

by Priyanka Kaushik, S. R. Nirmala
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 17
Year of Publication: 2018
Authors: Priyanka Kaushik, S. R. Nirmala
10.5120/ijca2018917874

Priyanka Kaushik, S. R. Nirmala . A Study on Speckle Noise Removal and Segmentation of Retinal Layers in OCT Image Analysis. International Journal of Computer Applications. 182, 17 ( Sep 2018), 25-33. DOI=10.5120/ijca2018917874

@article{ 10.5120/ijca2018917874,
author = { Priyanka Kaushik, S. R. Nirmala },
title = { A Study on Speckle Noise Removal and Segmentation of Retinal Layers in OCT Image Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 182 },
number = { 17 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 25-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number17/29955-2018917874/ },
doi = { 10.5120/ijca2018917874 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:42.276694+05:30
%A Priyanka Kaushik
%A S. R. Nirmala
%T A Study on Speckle Noise Removal and Segmentation of Retinal Layers in OCT Image Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 17
%P 25-33
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The survey paper shows the application of Optical Coherence Tomography images for detection of retinopathy. Image analysis methods enormously help in distinguishing different eye ailments. Currently, determination of retinal diseases depends mostly upon optical imaging techniques. Optical coherence tomography (OCT) is a routine diagnostic imaging method used worldwide in the evaluation of retinal diseases using the cross-sectional view of the retinal layers. The primary challenge in automatic identification and analysis of retinal disease cases is the presence of speckle noise and variation across edge boundaries. Due to the complexity of retinal structures, the tediousness of manual segmentation and variation from different specialists, many methods have been proposed to aid with this analysis. Therefore, efforts are being made to improve clinical decision making based on automated analysis of OCT data which will result in improving the accuracy, precision, and computational speed of segmentation methods, as well as reducing the amount of manual interaction.

References
  1. R. D. Chaudhari, A.A. Pawar and R.S. Deore, 2013, “The Historical Development Of Biometric Authentication Techniques: A Recent Overview”, International Journal of Engineering Research and Technology (IJERT), Vol.2, Issue 10.
  2. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, , 1991, “Optical coherence tomography,” Science, vol. 254, no. 5035, pp. 1178–1181.
  3. G.D. Hildebrand and A.R. Fielder, 2011, “Anatomy and Physiology of the Retina”, Springer-Verlag Berlin Heidelberg.
  4. J. Cheng, D. Tao, Y. Quan, D. W. K. Wong, Member, G.C. M. Cheung, M. Akiba and J. Liu, 2016, “Speckle Reduction in 3D Optical Coherence Tomography of Retina by A-Scan Reconstruction”, IEEE Transactions on Medical Imaging.
  5. A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, 2007, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. of Am. A, vol. 24(7), pp. 1901–1910.
  6. N. Anantrasirichai, L. Nicholson, J. E. Morgan, I.Erchovay, A. Achim, 2013 “Adaptive-Weighted Bilateral Filtering For Optical Coherence Tomography”,978-1-4799-2341-0/13/2013 IEEE, ICIP.
  7. B. Zhang and J.P. Allebach, 2007 ,“Adaptive bilateral filter for sharpness enhancement and noise removal,” in Image Processing, ICIP 2007. IEEE International Conference on, Oct. 2007, vol. 4, pp. IV –417 –IV – 420.
  8. N.Padmasini, K.S.Abbirame, R. Umamaheswari and S.Y. Mohamed, 2014 “Speckle Noise Reduction in Spectral Domain Optical Coherence Tomography Retinal Images Using Anisotropic Diffusion Filtering”, International Conference on Science, Engineering and Management Research (ICSEMR 2014).
  9. Yingtao Zhang, H.D.Cheng, JiaweiTian and JianhuaHuang a, XianglongTang, 2010, “Fractional subpixel diffusion and fuzzy logic approach for ultrasound speckle reduction”, in Elsevier journal of pattern recognition, Vol. 43, 2962-2970.
  10. A. M. Abhishek, T. T. J. M. Berendschot, S. V. Rao, and S. Dabir, 2014, “Segmentation and Analysis of Retinal Layers (ILM & RPE) in Optical Coherence Tomography Images with Edema”, IEEE Conference on Biomedical Engineering and Sciences, Miri, Sarawak, Malaysia.
  11. Raheleh Kafieh, Hossein Rabbani, Saeed Kermani, 2013, “A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina” Journal of Medical Signals & Sensors.
  12. S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt and S. Farsiu, 2010, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation”, Vol. 18, No. 18 / OPTICS EXPRESS 19413.
  13. A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, 2009,“Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17(26), 23719–23728.
  14. A. Lang, A. Carass,M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying,and J.L. Prince, 2013, “Retinal layer segmentation of macular OCT images using boundary Classification”, Vol. 4, No. 7 | DOI:10.1364/BOE.4.001133 | BIOMEDICAL OPTICS EXPRESS 1133.
  15. A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, 2013, “Segmentation of retinal OCT images using a random forest classifier,” Proc. SPIE 8669, 86690R.
  16. J. Duan, C. Tenchy, I. Gottlobz, F. Proudlockz, L. Bai, 2015, “Optical Coherence Tomography Image Segmentation”, 978-1-4799-8339-1/15 IEEE, ICIP, 2015.
  17. Ron Kimmel, Michael Elad, Doron Shaked, Renato Keshet, and Irwin Sobel, 2003,“A variational framework for retinex,” International Journal of computer vision, vol. 52, no. 1.
  18. Z.Amini and H. Rabbani, 2016, “Statistical Modeling of Retinal Optical Coherence Tomography”, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 35, NO. 6.
  19. D. Erdogmus, R. Jenssen, Y. N. Rao, and J. C. Principe, 2006, “Gaussianization: An efficient multivariate density estimation technique for statistical signal processing,” J. VLSI Signal Process. Syst. Signal, Image Video Technol., vol. 45, pp. 67–83.
  20. S. Roychowdhury, D. D. Koozekanani, S. Radwan and K. K. Parhi, 2013,“Automated Localization of Cysts in Diabetic Macular Edema using Optical Coherence Tomography Images”, 978-1-4577-0216-7/13, 35th Annual International Conference of the IEEE EMBS Osaka, Japan.
  21. B. Hassan and G. Raja, 2016, “Fully Automated Assessment of Macular Edema using Optical Coherence Tomography (OCT) Images”, 978-1-4673-8753-8/16/2016 IEEE.
  22. L. Zhang, W. Zhu, F. Shi, H. Chen, X. Chen, 2015, “Automated Segmentation Of Intraretinal Cystoid Macular Edema For Retinal 3D OCT Images With Macular Hole” International Symposium on Biomedical Imaging, vol. 12, pp. 1494 – 1497.
Index Terms

Computer Science
Information Sciences

Keywords

Optical Coherence Tomography Retinal Layers Image de-noising Image Segmentation Macular Edema