Retinal nerve fiber layer thickness map

Mircea Mujat, Raymond C. Chan, Barry Cense, Hyle Park, Chulmin Joo, Teresa C. Chen, Johannes F. De Boer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Spectral-Domain Optical Coherence Tomography (SDOCT) allows for in-vivo video-rate investigation of biomedical tissue depth structure with the purpose of non-invasive optical diagnostics. In ophthalmic applications, it has been suggested that Optical Coherence Tomography (OCT) can be used for diagnosis of glaucoma by measuring the thickness of the Retinal Nerve Fiber Layer (RNLF). We present here an automated method for determining the RNFL thickness map from a 3-D dataset. Boundary detection has been studied since the early days of computer vision and image processing, and different approaches have been proposed. The procedure described here is based on edge detection using a deformable spline (snake) algorithm. As the snake seeks to minimize its overall energy, its shape will converge on the image contour, the boundaries of the nerve fiber layer. In general, the snake is not allowed to travel too much, and therefore, proper initialization is required. The snake parameters, elasticity, rigidity, viscosity, and external force weight are set to allow the snake to follow the boundary for a large number of retinal topographies. The RNFL thickness map is combined with an integrated reflectance map of the retina and retinal cross-sectional images (OCT movie), to provide the ophthalmologist with a familiar image for interpreting the OCT data. The video-rate capabilities of our SDOCT system allow for mapping the true retinal topography since the motion artifacts are significantly reduced as compared to slower time-domain systems.

Original languageEnglish
Title of host publicationOphthalmic Technologies XVI
DOIs
Publication statusPublished - 2006 Jun 30
EventOphthalmic Technologies XVI - San Jose, CA, United States
Duration: 2006 Jan 212006 Jan 24

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume6138
ISSN (Print)1605-7422

Other

OtherOphthalmic Technologies XVI
CountryUnited States
CitySan Jose, CA
Period06/1/2106/1/24

Fingerprint

Optical tomography
Fibers
Topography
Edge detection
Rigidity
Splines
Computer vision
Elasticity
Image processing
Viscosity
Tissue

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Mujat, M., Chan, R. C., Cense, B., Park, H., Joo, C., Chen, T. C., & De Boer, J. F. (2006). Retinal nerve fiber layer thickness map. In Ophthalmic Technologies XVI [61380J] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 6138). https://doi.org/10.1117/12.649064
Mujat, Mircea ; Chan, Raymond C. ; Cense, Barry ; Park, Hyle ; Joo, Chulmin ; Chen, Teresa C. ; De Boer, Johannes F. / Retinal nerve fiber layer thickness map. Ophthalmic Technologies XVI. 2006. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{68ef52812221495d91bbf5e53d3f37ed,
title = "Retinal nerve fiber layer thickness map",
abstract = "Spectral-Domain Optical Coherence Tomography (SDOCT) allows for in-vivo video-rate investigation of biomedical tissue depth structure with the purpose of non-invasive optical diagnostics. In ophthalmic applications, it has been suggested that Optical Coherence Tomography (OCT) can be used for diagnosis of glaucoma by measuring the thickness of the Retinal Nerve Fiber Layer (RNLF). We present here an automated method for determining the RNFL thickness map from a 3-D dataset. Boundary detection has been studied since the early days of computer vision and image processing, and different approaches have been proposed. The procedure described here is based on edge detection using a deformable spline (snake) algorithm. As the snake seeks to minimize its overall energy, its shape will converge on the image contour, the boundaries of the nerve fiber layer. In general, the snake is not allowed to travel too much, and therefore, proper initialization is required. The snake parameters, elasticity, rigidity, viscosity, and external force weight are set to allow the snake to follow the boundary for a large number of retinal topographies. The RNFL thickness map is combined with an integrated reflectance map of the retina and retinal cross-sectional images (OCT movie), to provide the ophthalmologist with a familiar image for interpreting the OCT data. The video-rate capabilities of our SDOCT system allow for mapping the true retinal topography since the motion artifacts are significantly reduced as compared to slower time-domain systems.",
author = "Mircea Mujat and Chan, {Raymond C.} and Barry Cense and Hyle Park and Chulmin Joo and Chen, {Teresa C.} and {De Boer}, {Johannes F.}",
year = "2006",
month = "6",
day = "30",
doi = "10.1117/12.649064",
language = "English",
isbn = "0819461814",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
booktitle = "Ophthalmic Technologies XVI",

}

Mujat, M, Chan, RC, Cense, B, Park, H, Joo, C, Chen, TC & De Boer, JF 2006, Retinal nerve fiber layer thickness map. in Ophthalmic Technologies XVI., 61380J, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 6138, Ophthalmic Technologies XVI, San Jose, CA, United States, 06/1/21. https://doi.org/10.1117/12.649064

Retinal nerve fiber layer thickness map. / Mujat, Mircea; Chan, Raymond C.; Cense, Barry; Park, Hyle; Joo, Chulmin; Chen, Teresa C.; De Boer, Johannes F.

Ophthalmic Technologies XVI. 2006. 61380J (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 6138).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Retinal nerve fiber layer thickness map

AU - Mujat, Mircea

AU - Chan, Raymond C.

AU - Cense, Barry

AU - Park, Hyle

AU - Joo, Chulmin

AU - Chen, Teresa C.

AU - De Boer, Johannes F.

PY - 2006/6/30

Y1 - 2006/6/30

N2 - Spectral-Domain Optical Coherence Tomography (SDOCT) allows for in-vivo video-rate investigation of biomedical tissue depth structure with the purpose of non-invasive optical diagnostics. In ophthalmic applications, it has been suggested that Optical Coherence Tomography (OCT) can be used for diagnosis of glaucoma by measuring the thickness of the Retinal Nerve Fiber Layer (RNLF). We present here an automated method for determining the RNFL thickness map from a 3-D dataset. Boundary detection has been studied since the early days of computer vision and image processing, and different approaches have been proposed. The procedure described here is based on edge detection using a deformable spline (snake) algorithm. As the snake seeks to minimize its overall energy, its shape will converge on the image contour, the boundaries of the nerve fiber layer. In general, the snake is not allowed to travel too much, and therefore, proper initialization is required. The snake parameters, elasticity, rigidity, viscosity, and external force weight are set to allow the snake to follow the boundary for a large number of retinal topographies. The RNFL thickness map is combined with an integrated reflectance map of the retina and retinal cross-sectional images (OCT movie), to provide the ophthalmologist with a familiar image for interpreting the OCT data. The video-rate capabilities of our SDOCT system allow for mapping the true retinal topography since the motion artifacts are significantly reduced as compared to slower time-domain systems.

AB - Spectral-Domain Optical Coherence Tomography (SDOCT) allows for in-vivo video-rate investigation of biomedical tissue depth structure with the purpose of non-invasive optical diagnostics. In ophthalmic applications, it has been suggested that Optical Coherence Tomography (OCT) can be used for diagnosis of glaucoma by measuring the thickness of the Retinal Nerve Fiber Layer (RNLF). We present here an automated method for determining the RNFL thickness map from a 3-D dataset. Boundary detection has been studied since the early days of computer vision and image processing, and different approaches have been proposed. The procedure described here is based on edge detection using a deformable spline (snake) algorithm. As the snake seeks to minimize its overall energy, its shape will converge on the image contour, the boundaries of the nerve fiber layer. In general, the snake is not allowed to travel too much, and therefore, proper initialization is required. The snake parameters, elasticity, rigidity, viscosity, and external force weight are set to allow the snake to follow the boundary for a large number of retinal topographies. The RNFL thickness map is combined with an integrated reflectance map of the retina and retinal cross-sectional images (OCT movie), to provide the ophthalmologist with a familiar image for interpreting the OCT data. The video-rate capabilities of our SDOCT system allow for mapping the true retinal topography since the motion artifacts are significantly reduced as compared to slower time-domain systems.

UR - http://www.scopus.com/inward/record.url?scp=33745368699&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745368699&partnerID=8YFLogxK

U2 - 10.1117/12.649064

DO - 10.1117/12.649064

M3 - Conference contribution

AN - SCOPUS:33745368699

SN - 0819461814

SN - 9780819461810

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Ophthalmic Technologies XVI

ER -

Mujat M, Chan RC, Cense B, Park H, Joo C, Chen TC et al. Retinal nerve fiber layer thickness map. In Ophthalmic Technologies XVI. 2006. 61380J. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.649064