Literature DB >> 19724565

Automated segmentation of the macula by optical coherence tomography.

Tapio Fabritius1, Shuichi Makita, Masahiro Miura, Risto Myllylä, Yoshiaki Yasuno.   

Abstract

This paper presents optical coherence tomography (OCT) signal intensity variation based segmentation algorithms for retinal layer identification. Its main ambition is to reduce the calculation time required by layer identification algorithms. Two algorithms, one for the identification of the internal limiting membrane (ILM) and the other for retinal pigment epithelium (RPE) identification are implemented to evaluate structural features of the retina. Using a 830 nm spectral domain OCT device, this paper demonstrates a segmentation method for the study of healthy and diseased eyes.

Mesh:

Year:  2009        PMID: 19724565     DOI: 10.1364/OE.17.015659

Source DB:  PubMed          Journal:  Opt Express        ISSN: 1094-4087            Impact factor:   3.894


  36 in total

1.  Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis.

Authors:  Delia Cabrera Debuc; Harry M Salinas; Sudarshan Ranganathan; Erika Tátrai; Wei Gao; Meixiao Shen; Jianhua Wang; Gábor M Somfai; Carmen A Puliafito
Journal:  J Biomed Opt       Date:  2010 Jul-Aug       Impact factor: 3.170

2.  Automated segmentation of intramacular layers in Fourier domain optical coherence tomography structural images from normal subjects.

Authors:  Xusheng Zhang; Siavash Yousefi; Lin An; Ruikang K Wang
Journal:  J Biomed Opt       Date:  2012-04       Impact factor: 3.170

3.  A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes.

Authors:  Bhavna J Antony; Michael D Abràmoff; Matthew M Harper; Woojin Jeong; Elliott H Sohn; Young H Kwon; Randy Kardon; Mona K Garvin
Journal:  Biomed Opt Express       Date:  2013-11-01       Impact factor: 3.732

4.  RefMoB, a Reflectivity Feature Model-Based Automated Method for Measuring Four Outer Retinal Hyperreflective Bands in Optical Coherence Tomography.

Authors:  Douglas H Ross; Mark E Clark; Pooja Godara; Carrie Huisingh; Gerald McGwin; Cynthia Owsley; Katie M Litts; Richard F Spaide; Kenneth R Sloan; Christine A Curcio
Journal:  Invest Ophthalmol Vis Sci       Date:  2015-07       Impact factor: 4.799

5.  Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks.

Authors:  Freerk G Venhuizen; Bram van Ginneken; Bart Liefers; Mark J J P van Grinsven; Sascha Fauser; Carel Hoyng; Thomas Theelen; Clara I Sánchez
Journal:  Biomed Opt Express       Date:  2017-06-16       Impact factor: 3.732

6.  Automatic and robust segmentation of endoscopic OCT images and optical staining.

Authors:  Jianlin Zhang; Wu Yuan; Wenxuan Liang; Shanyong Yu; Yanmei Liang; Zhiyong Xu; Yuxing Wei; Xingde Li
Journal:  Biomed Opt Express       Date:  2017-04-26       Impact factor: 3.732

7.  Automated segmentation algorithm for detection of changes in vaginal epithelial morphology using optical coherence tomography.

Authors:  Shahab Chitchian; Kathleen L Vincent; Gracie Vargas; Massoud Motamedi
Journal:  J Biomed Opt       Date:  2012-11       Impact factor: 3.170

8.  Automatic segmentation of choroidal thickness in optical coherence tomography.

Authors:  David Alonso-Caneiro; Scott A Read; Michael J Collins
Journal:  Biomed Opt Express       Date:  2013-11-11       Impact factor: 3.732

9.  Association of Structural and Functional Measures With Contrast Sensitivity in Glaucoma.

Authors:  Nima Fatehi; Sara Nowroozizadeh; Sharon Henry; Anne L Coleman; Joseph Caprioli; Kouros Nouri-Mahdavi
Journal:  Am J Ophthalmol       Date:  2017-03-23       Impact factor: 5.258

10.  Advances in the Structural Evaluation of Glaucoma with Optical Coherence Tomography.

Authors:  Daniel Meira-Freitas; Renato Lisboa; Felipe A Medeiros
Journal:  Curr Ophthalmol Rep       Date:  2013-06-01
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