Method for segmentation of the layers in the outer retina

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn 2015-November, Issue , 2015, Pages 5646-5649

  Novosel, Jelena ^a   Vermeer, Koenraad A ^a   Pierrache, Laurence ^a   Klaver, Caroline C W ^b   Van Den Born, L I ^c   Van Vliet, Lucas J ^d  

^a Rotterdam Ophthalmic Institute   (Netherlands)

^b ERASMUS MEDICAL CENTER   (Netherlands)

^c ROTTERDAM EYE HOSPITAL   (Netherlands)

^d DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; OPTICAL COHERENCE TOMOGRAPHY; RETINA; RETINITIS PIGMENTOSA; STATISTICAL MODEL;

HUMANS; LIKELIHOOD FUNCTIONS; RETINA; RETINITIS PIGMENTOSA; TOMOGRAPHY, OPTICAL COHERENCE;

EID: 84953295607     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EMBC.2015.7319673     Document Type: Conference Paper

References (14)

1. Huang, D. et al., Optical coherence tomography, Science, 1991, 254, pp. 1178-1181.
2. Spaide R. F. and Curcio C. A., Anatomical correlates to the bands seen in the outer retina by optical coherence tomography, Retina, 2011, 31(8), pp. 1609-1619.
3. Kafieh, R. et al., A review of algorithms for segmentation of optical coherence tomography from retina, J. Med. Signals Sens., 2013, 3, pp. 45-60.
4. Lang, A. et al., Retinal layer segmentation of macular OCT images using boundary classification, Biomed. Opt. Express, 2013, 4, pp. 1133-1152.
5. Yang Q. et al., Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa, Biomed. Opt. Express, 2011, 2(9), pp. 2493-2503.
6. Srinivasan P. P. et al., Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology, Biomed. Opt. Express, 2014, 5(2), pp. 348-365.
7. Vermeer, K. A. et al., Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography, Biomed. Opt. Express, 2013, 5, pp. 322-337.
8. Neyman, J. et al., On the Problem of the Most Efficient Tests of Statistical Hypotheses, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1933, 231, pp. 289-337.
9. Akaike, H., A new look at the statistical model identification, IEEE Trans. on Automat. Contr, 1973, 19, pp. 716-723
10. Bozdogan, H., ICOMP: A new model-selection criterion, Classification and related methods of data analysis, 1988, pp. 599-608.
11. Fisher, R. A., On the mathematical foundations of theoretical statistics, 1922, 222, pp. 309-368.
12. Novosel J. et al., Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography, 2015, Manuscript under review
13. Bakker, P. et al., Edge preserving orientation adaptive filtering, In Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 1999, 1, pp. 535-540.
14. Harris C. and Stephens M., A combined corner and edge detector, Proc. of 4th Alvey Vision Conf., 1988, pp. 147-151.