Journal of Computer Science and Technology

, Volume 32, Issue 6, pp 1214–1221 | Cite as

Automatic Anterior Lamina Cribrosa Surface Depth Measurement Based on Active Contour and Energy Constraint

  • Zai-Liang Chen
  • Peng Peng
  • Bei-Ji Zou
  • Hai-Lan ShenEmail author
  • Hao Wei
  • Rong-Chang Zhao
Regular Paper


The lamina cribrosa is affected by intraocular pressure, which is the major risk of glaucoma. However, the capability to evaluate the lamina cribrosa in vivo has been limited until recently due to poor image quality and the posterior laminar displacement of glaucomatous eyes. In this study, we propose an automatic method to measure the anterior lamina cribrosa surface depth (ALCSD), including a method for detecting Bruch’s membrane opening (BMO) based on k-means and region-based active contour. An anterior lamina cribrosa surface segmentation method based on energy constraint is also proposed. In BMO detection, we initialize the Chan-Vese active contour model by using the segmentation map of the k-means cluster. In the segmentation of anterior lamina cribrosa surface, we utilize the energy function in each A-scan to establish a set of candidates. The points in the set that fail to meet the constraints are removed. Finally, we use the B-spline fitting method to obtain the results. The proposed automatic method can model the posterior laminar displacement by measuring the ALCSD. This method achieves a mean error of 45.34 μm in BMO detection. The mean errors of the anterior lamina cribrosa surface are 94.1% within five pixels and 76.1% within three pixels.


anterior lamina cribrosa surface segmentation active contour energy constraint measurement 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11390_2017_1795_MOESM1_ESM.pdf (744 kb)
ESM 1 (PDF 744 kb)


  1. 1.
    Cook C, Foster P. Epidemiology of glaucoma: What’s new? Canadian Journal of Ophthalmology, 2012, 47(3): 223-226.CrossRefGoogle Scholar
  2. 2.
    Minckler D S, Bunt A H, Johanson G W. Orthograde and retrograde axoplasmic transport during acute ocular hypertension in the monkey. Investigative Ophthalmology & Visual Science, 1977, 16(5): 426-441.Google Scholar
  3. 3.
    Quigley H A, Addicks E M. Regional differences in the structure of the lamina cribrosa and their relation to glaucomatous optic nerve damage. Archives of Ophthalmology, 1981, 99(1): 137-143.CrossRefGoogle Scholar
  4. 4.
    Yan D B, Coloma F M, Metheetrairut A, Trope G E, Heathcote J G, Ethier C R. Deformation of the lamina cribrosa by elevated intraocular pressure. British Journal of Ophthalmology, 1994, 78(8): 643-648.CrossRefGoogle Scholar
  5. 5.
    Jonas J B, Wang N, Yang D. Translamina cribrosa pressure difference as potential element in the pathogenesis of glaucomatous optic neuropathy. The Asia-Pacific Journal of Ophthalmology, 2016, 5(1): 5-10.CrossRefGoogle Scholar
  6. 6.
    Reis A S C, O’Leary N, Stanfield M J, Shuba L M, Nicolela M T, Chauhan B C. Laminar displacement and prelaminar tissue thickness change after glaucoma surgery imaged with optical coherence tomography. Investigative Ophthalmology & Visual Science, 2012, 53(9): 5819-5826.CrossRefGoogle Scholar
  7. 7.
    Seo J H, Kim T W, Weinreb R N. Lamina cribrosa depth in healthy eyes. Investigative Ophthalmology & Visual Science, 2014, 55(3): 1241-1251.CrossRefGoogle Scholar
  8. 8.
    Spaide R F, Koizumi H, Pozzoni M C. Enhanced depth imaging spectral-domain optical coherence tomography. American Journal of Ophthalmology, 2008, 146(4): 496-500.CrossRefGoogle Scholar
  9. 9.
    Abe R Y, Gracitelli C P B, Diniz-Filho A, Tatham A J, Medeiros F A. Lamina cribrosa in glaucoma: Diagnosis and monitoring. Current Ophthalmology Reports, 2015, 3(2): 74-84.CrossRefGoogle Scholar
  10. 10.
    Furlanetto R L, Park S C, Damle U J, Sieminski S F, Kung Y, Siegal N, Ritch R. Posterior displacement of the lamina cribrosa in glaucoma: In vivo interindividual and intereye comparisons. Investigative Ophthalmology & Visual Science, 2013, 54(7): 4836-4842.CrossRefGoogle Scholar
  11. 11.
    Miri M S, Robles V A, Abrmoff M D, Kwon Y H, Garvin M K. Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes. Computerized Medical Imaging and Graphics, 2017, 55: 87-94.CrossRefGoogle Scholar
  12. 12.
    Shah A, Wang J K, Garvin M K, Sonka M, Wu X. Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3-D range expansion approach. In Proc. the 11th IEEE International Symposium on Biomedical Imaging (ISBI), Apr. 29-May 2, 2014, pp.1405-1408.Google Scholar
  13. 13.
    Lu S, Cheung C Y L, Liu J, Lim J H, Leung C K S, Wong T Y. Automated layer segmentation of optical coherence tomography images. IEEE Transactions on Biomedical Engineering, 2010, 57(10): 2605-2608.CrossRefGoogle Scholar
  14. 14.
    Belghith A, Bowd C, Medeiros F A, Weinreb R N, Zangwill L M. Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes? In Proc. the 12th IEEE International Symposium on Biomedical Imaging (ISBI), Apr. 2015, pp.222-225.Google Scholar
  15. 15.
    Chan T F, Vese L A. Active contours without edges. IEEE Transactions on Image Processing, 2001, 10(2): 266-277.CrossRefzbMATHGoogle Scholar
  16. 16.
    GirardM J, Strouthidis N G, Ethier C R, Mari J M. Shadow removal and contrast enhancement in optical coherence tomography images of the human optic nerve head. Investigative Ophthalmology & Visual Science, 2011, 52(10): 7738-7748.CrossRefGoogle Scholar
  17. 17.
    Foin N, Mari J M, Davies J E, Di Mario C, Girard M J. Imaging of coronary artery plaques using contrast-enhanced optical coherence tomography. European Heart Journal–Cardiovascular Imaging, 2013, 14(1): 85.CrossRefGoogle Scholar
  18. 18.
    Zhang Q, Wang Y X, Li J J et al. Optical coherence tomography of prelaminar tissue and its relationship with oculopathy. International Review of Ophthalmology, 2017, 41(1): 8-13. (in Chinese)Google Scholar
  19. 19.
    HussainM A, Bhuiyan A, Ramamohanarao K. Disc segmentation and BMO-MRW measurement from SD-OCT image using graph search and tracing of three bench mark reference layers of retina. In Proc. IEEE International Conference on Image Processing (ICIP), Sept. 2015, pp.4087-4091.Google Scholar
  20. 20.
    Chang J, Fisher J W. Efficient MCMC sampling with implicit shape representations. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2011, pp.2081-2088.Google Scholar
  21. 21.
    Ren R, Yang H, Gardiner S K, Fortune B, Hardin C, Demirel S, Burgoyne C F. Anterior lamina cribrosa surface depth, age, and visual field sensitivity in the Portland progression project. Investigative Ophthalmology & Visual Science, 2014, 55(3): 1531-1539.CrossRefGoogle Scholar
  22. 22.
    Cheung C Y, Chen D, Wong T Y et al. Determinants of quantitative optic nerve measurements using spectral domain optical coherence tomography in a population-based sample of non-glaucomatous subjects. Investigative Ophthalmology & Visual Science, 2011, 52(13): 9629-9635.CrossRefGoogle Scholar
  23. 23.
    Patel N B, Lim M, Gajjar A, Evans K B, Harwerth R S. Age-associated changes in the retinal nerve fiber layer and optic nerve head. Investigative Ophthalmology & Visual Science, 2014, 55(8): 5134-5143.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2017

Authors and Affiliations

  • Zai-Liang Chen
    • 1
    • 2
    • 3
  • Peng Peng
    • 1
    • 2
  • Bei-Ji Zou
    • 1
    • 2
    • 3
  • Hai-Lan Shen
    • 1
    • 3
    Email author
  • Hao Wei
    • 1
    • 2
  • Rong-Chang Zhao
    • 1
    • 3
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Center for Ophthalmic Imaging ResearchCentral South UniversityChangshaChina
  3. 3.“Mobile Health” Ministry of Education-China Mobile Joint LaboratoryCentral South UniversityChangshaChina

Personalised recommendations