Open Access Journal

ISSN : 2394 - 6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Segmentation of Fluid Regions in Patients Having Diabetic Macular Edema Using Graph Algorithm and Neutrosophic Sets In Optical Coherence Tomography Images

Author : Anupriya.G 1 Usha.A 2

Date of Publication :30th April 2018

Abstract: Optical coherence tomography (OCT) is the latest imaging technology with applications in medicine, biology as well as materials investigations. Over the time, diabetes can lead to serious problems in the blood vessels, retina, nerves, brain and eyes. These problems can lead to Diabetic macular edema (DME) causing the problem in the eye area to resolve this, Segmentation of fluid in optical coherence tomography images is implemented. Segmentation of OCT images is done using neutrosophic set and graph algorithm. Graph algorithm is applied to the neutrosophic set in order to segment the inner layers of the eye and identify the fluid regions. Cluster computation is done using the cost function method to implement cluster based fluid segmentation. Finally, by ignoring the very small regions and middle layers finally the fluid regions are obtained for the diagnosis of DME.

Reference :

    1. 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 et al., “Optical coherence tomography,” Science (New York, NY), vol. 254, no. 5035, p. 1178, 1991.
    2. R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Medical image analysis, vol. 17, no. 8, pp. 907– 928, 2013
    3. S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, and J. A. Izatt, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomedical optics express, vol. 6, no. 4, pp. 1172–1194, 2015.
    4. J. W. Yau, S. L. Rogers, R. Kawasaki, E. L. Lamoureux, J. W. Kowalski, T. Bek, S.-J. Chen, J. M. Dekker, A. Fletcher, and J. Grauslund, “Global prevalence and major risk factors of diabetic retinopathy,” Diabetes care, vol. 35, no. 3, pp. 556–564, 2012.
    5. J. Pe’er, R. Folberg, A. Itin, H. Gnessin, I. Hemo, and E. Keshet, “Upregulated expression of vascular endothelial growth factor in proliferative diabetic retinopathy.” British journal of ophthalmology, vol. 80, no. 3, pp. 241–245, 1996.
    6. D. C. DeBuc, A review of algorithms for segmentation of retinal image data using optical coherence tomography. INTECH Open Access Publisher, 2011.
    7. F. Smarandache, A Unifying Field in Logics Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability. American Research Press, 2005.
    8. H.-D. Cheng and Y.Guo, “New Neutrosophic approach to image segmentation,” in Pattern Recognition, vol. 42, no. 5, pp. 587–595, 2009.
    9. M. Zhang, L. Zhang, and H. Cheng, “A neutrosophic approach to image segmentation based on watershed method,” Signal Processing, vol. 90, no. 5, pp. 1510–1517, 2010.
    10. A. Sengur and Y. Guo, “Color texture image segmentation based on neutrosophic set and wavelet transformation,” Computer Vision and Image Understanding, vol. 115, no. 8, pp. 1134–1144, 2011.
    11. A. Heshmati, M. Gholami, and A. Rashno, “Scheme for unsupervised colour–texture image segmentation using neutrosophic set and nonsubsampled contourlet transform,” IET Image Processing, vol. 10, no. 6, pp. 464–473, 2016
    12. Y. Guo, A. S¸engur, and J. Ye, “A novel image thresholding algorithm¨ based on neutrosophic similarity score,” Measurement, vol. 58, pp. 175– 186, 2014.
    13. J. Shan, H. Cheng, and Y. Wang, “A novel segmentation method for breast ultrasound images based on neutrosophic lmeans clustering,” Medical physics, vol. 39, no. 9, pp. 5669– 5682, 2012
    14. Y. Guo and A. S¸engur, “A novel image edge detection algorithm based¨ on neutrosophic set,” Computers & Electrical Engineering, vol. 40, no. 8, pp. 3–25, 2014.
    15. Y. Guo and A. Sengur, “Ncm: Neutrosophic c-means clustering algorithm,” Pattern Recognition, vol. 48, no. 8, pp. 2710–2724, 2015.
    16. B. Peng, L. Zhang, and D. Zhang, “A survey of graph theoretical approaches to image segmentation,” Pattern Recognition, vol. 46, no. 3, pp. 1020–1038, 2013.
    17. M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. Russell, X. Wu, and` M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE transactions on medical imaging, vol. 27, no. 10, pp. 1495–1505, 2008
    18. Q. Yang, C.A. Riesman. Wang,Y. Fukuma, ,M.Hangai, N. Yoshimura, A. Tomidokoro, M. Araie, A. S. Raza, D. C. Hood et al., “Automated layer segmentation of macular oct images using dual-scale gradient information,” Optics express, vol. 18, no. 20, pp. 21293–21307,
    19. S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation,” Optics express, vol. 18, no. 18, pp. 19413–19428, 2010.
    20. K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE transactions on medical imaging, vol. 29, no. 1, pp. 159– 168, 2010.
    21. S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in sd-oct images,” Investigative ophthalmology & visual science, vol. 53, no. 1, pp. 53–61, 2012.
    22. K. K. Parhi, A. Rashno, B. Nazari, S. Sadri, H. Rabbani, P. Drayna, and D. D. Koozekanani, “Automated fluid/cyst segmentation: A quantitative assessment of diabetic macular edema,” Investigative Ophthalmology & Visual Science, vol. 58, no. 8, pp. 4633–4633, 2017.

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