http://iet.metastore.ingenta.com
1887

Automatic choroid layer segmentation using normalized graph cut

Automatic choroid layer segmentation using normalized graph cut

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Optical coherence tomography is an immersive technique for depth analysis of retinal layers. Automatic choroid layer segmentation is a challenging task because of the low contrast inputs. Existing methodologies carried choroid layer segmentation manually or semi-automatically. The authors proposed automated choroid layer segmentation based on normalised cut algorithm, which aims at extracting the global impression of images and treats the segmentation as a graph partitioning problem. Due to the structure complexity of retinal and choroid layers, the authors employed a series of pre-processing to make the cut more deterministic and accurate. The proposed method divided the image into several patches and ran the normalised cut algorithm on every patch separately. The aim was to avoid insignificant vertical cuts and focus on horizontal cutting. After processing every patch, the authors acquired a global cut on the original image by combining all the patches. Later the authors measured the choroidal thickness which is highly helpful in the diagnosis of several retinal diseases. The results were computed on a total of 525 images of 21 real patients. Experimental results showed that the mean relative error rate of the proposed method was around 0.4 when compared with the manual segmentation performed by the experts.

References

    1. 1)
      • 1. Tian, J., Marziliano, P., Baskaran, M., et al: ‘Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images’, Biomed. Opt. Express, 2013, 4, (3), pp. 397411.
    2. 2)
      • 2. Yongjian, Y., Acton, S.T.: ‘Speckle reducing anisotropic diffusion’, IEEE Trans. Image Process., 2002, 11, (11), pp. 12601270.
    3. 3)
      • 3. Shi, J., Malik, J.: ‘Normalized cuts and image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (8), pp. 888905.
    4. 4)
      • 4. Delori, F.C., Gragoudas, E.S.: ‘Monochromatic ophthalmoscopy and fundus photography: the normal fundus’, Arch. Ophthalmol., 1977, 95, pp. 861868.
    5. 5)
      • 5. Novotny, H.R., Alvis, D.L.: ‘A method of photographing fluorescence in circulating blood in the human retina’, Circulation, 1961, 24, pp. 8286.
    6. 6)
      • 6. Zeimer, R., Shahidi, M.: ‘A new method for rapid mapping of the retinal thickness at the posterior pole’, Invest. Ophthalmol. Vis. Sci., 1996, 37, pp. 19942001.
    7. 7)
      • 7. Rossant, F., Ghorbel, I., Bloch, I., et al: ‘Automated segmentation of retinal layers in OCT imaging and derived ophthalmic measures’. 2009 IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, IEEE, 2009, pp. 13701373.
    8. 8)
      • 8. George, A., Dillenseger, J.A., Weber, A., et al: ‘Optical coherence tomography image processing’, Invest. Ophthalmol. Vis. Sci., 2000, 41, pp. 165173.
    9. 9)
      • 9. Baroni, M., Fortunato, J.G., Torre, A.L.: ‘Towards quantitative analysis of retinal features in optical coherence tomography’, Med. Eng. Phys., 2007, 29, pp. 432441.
    10. 10)
      • 10. Bagci, A.M., Shahidi, M., Ansari, R., et al: ‘Thickness profile of retinal layers by opticalcoherence tomography image segmentation’, Am. J. Ophthalmol., 2008, 146, pp. 679687.
    11. 11)
      • 11. Fuller, A.R., Zawadzki, R.J., Choi, S., et al: ‘Segmentation of three-dimensional retinal image data’, IEEE Trans. Vis. Comput. Graph., 2007, 13, pp. 17191726.
    12. 12)
      • 12. Quellec, G., Lee, K., Dolejsi, M., et al: ‘Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula’, IEEE Trans. Med. Imaging, 2010, 29, pp. 13211330.
    13. 13)
      • 13. Kafieh, R., Rabbani, H., Foroohandeh, M.: ‘Circular symmetric Laplacian mixture model in wavelet diffusion for dental image denoising’, J Med Signals Sens., 2012, 2, (2), pp. 103111.
    14. 14)
      • 14. Yazdanpanah, A., Hamarneh, G., Smith, B., et al: ‘Intra-retinal layer segmentation in optical coherence tomography using an active contour approach’, Med. Image Comput. Comput. Assist. Interv., 2009, 12, pp. 649656.
    15. 15)
      • 15. Yang, Q., Reisman, C.A., Wang, Z., et al: ‘Automated layer segmentation of macular OCT images using dual-scale gradient information’, Opt. Express, 2010, 18, pp. 2129421307.
    16. 16)
      • 16. Kafieh, R., Rabbani, H., Abramoff, M.D., et al: ‘Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map’, Med. Image Anal., 2012.
    17. 17)
      • 17. Hood, D.C., Fortune, B., Arthur, S.N., et al: ‘Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography’, J. Glaucoma, 2008, 17, pp. 519528.
    18. 18)
      • 18. Fernández, D.C., Villate, N., Puliafito, C.A., et al: ‘Comparing total macular volume changes measured by optical coherence tomography with retinal lesion volume estimated by active contours’, Invest. Ophthalmol. Vis. Sci., 2004, 45 E-Abstract 3072.
    19. 19)
      • 19. Mishra, A., Wong, A., Bizheva, K., et al: ‘Intra-retinal layer segmentation in optical coherence tomography images’, Opt. Express, 2009, 17, pp. 2371923728.
    20. 20)
      • 20. Mayer, M.A., Tornow, R.P., Bock, R., et al: ‘Automatic nerve fiber layer segmentation and geometry correction on spectral domain OCT images using fuzzy c-means clustering’. The Association for Research in Vision and Ophthalmology, Inc. (ARVO) (Annual Meeting), Fort Lauderdale, Florida, USA, 2008.
    21. 21)
      • 21. Vermeer, K.A., van der Schoot, J., Lemij, H.G., et al: ‘Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images’, Biomed. Opt. Express, 2011, 2, pp. 17431756.
    22. 22)
      • 22. Garvin, M.K., Abramoff, M.D., Kardon, R., et al: ‘Intraretinal layer segmentation of macular optical coherence tomography images using optimal3–D graph search’, IEEE Trans. Med. Imaging, 2008, 27, pp. 14951505.
    23. 23)
      • 23. Abràmoff, M.D., Lee, K., Niemeijer, M., et al: ‘Automated segmentation of the cup and rim from spectral domain OCT of the optic nerve head’, Invest. Ophthalmol. Vis. Sci., 2009, 50, pp. 57785784.
    24. 24)
      • 24. Hee, M.R., Izatt, J.A., Swanson, E.A., et al: ‘Optical coherence tomography of the human retina’, Arch. Ophthalmol., 1995, 113, pp. 325332.
    25. 25)
      • 25. Lindner, M., Bezatis, A., Czauderna, J., et al: ‘Choroidal thickness in geographic atrophy secondary to age-related macular degeneration choroidal thickness in geographic atrophy’, Invest. Ophthalmol. Vis. Sci., 2015, 56, (2), pp. 875882.
    26. 26)
      • 26. Hanumunthadu, D., Ilginis, T., Restori, M., et al: ‘Spectral-domain optical coherence tomography retinal and choroidal thickness metric repeatability in age-related macular degeneration’, Am. J. Ophthalmol., 2016, 166, pp. 154161.
    27. 27)
      • 27. Harb, E., Hyman, L., Gwiazda, J., et al: ‘COMET study group. choroidal thickness profiles in myopic eyes of young adults in the correction of myopia evaluation trial cohort’, Am. J. Ophthalmol., 2015, 160, (1), pp. 6271.
    28. 28)
      • 28. Tan, C.S., Cheong, K.X., Lim, L.W., et al: ‘Comparison of macular choroidal thicknesses from swept source and spectral domain optical coherence tomography’, Br. J. Ophthalmol., 2016, 100, (7), pp. 995999.
    29. 29)
      • 29. Zhang, L., Buitendijk, G.H., Lee, K., et al: ‘Validity of automated choroidal segmentation in SS-OCT and SD-OCT Choroidal segmentation in SS-OCT and SD-OCT’, Invest. Ophthalmol. Vis. Sci., 2015, 56, (5), pp. 32023211.
    30. 30)
      • 30. Mazzaferri, J., Beaton, L., Hounye, G., et al: ‘Open-source algorithm for automatic choroid segmentation of OCT volume reconstructions’, Sci. Rep., 2017, 7.
    31. 31)
      • 31. Beaton, L., Mazzaferri, J., Lalonde, F., et al: ‘Non-invasive measurement of choroidal volume change and ocular rigidity through automated segmentation of high-speed OCT imaging’, Biomed. Opt. Express, 2015, 6, (5), pp. 16941706.
    32. 32)
      • 32. Wang, C., Li, Y., Wang, Y.X.: ‘Automatic choroidal layer segmentation using Markov random field and level set method’, IEEE J. Biomed. Health Inf., 2017.
    33. 33)
      • 33. De Boor, C.: ‘A practical guide to splines’, Math. Comput., 1978.
    34. 34)
      • 34. Alonso-Caneiro, D., Read, S.A., Collins, M.J.: ‘Automatic segmentation of choroidal thickness in optical coherence tomography’, Biomed. Opt. Express, 2013, 4, (12), pp. 27952812.
    35. 35)
      • 35. Danesh, H., Kafieh, R., Rabbani, H., et al: ‘Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts’, Comput. Math. Methods Med., 2014.
    36. 36)
      • 36. Kajić, V., Esmaeelpour, M., Považay, B., et al: ‘Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model’, Biomed. Opt. Express, 2012, 3, (1), pp. 86103.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0273
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0273
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address