NDLI: Method for segmentation of the layers in the outer retina

Content Provider	IEEE Xplore Digital Library
Author	Novosel, J. Vermeer, K.A. Pierrache, L. Klaver, C.C.W. van den Born, L.I. van Vliet, L.J.
Copyright Year	2015
Description	Author affiliation: Dept. of Imaging Phys., Delft Univ. of Technol., Delft, Netherlands (van Vliet, L.J.) \|\| Rotterdam Ophthalmic Inst., Rotterdam, Netherlands (Novosel, J.; Vermeer, K.A.; Pierrache, L.) \|\| Dept. of Ophthalmology, Erasmus Med. Centre, Rotterdam, Netherlands (Klaver, C.C.W.) \|\| Rotterdam Ophthalmic Inst., Rotterdam Eye Hosp., Rotterdam, Netherlands (van den Born, L.I.)
Abstract	This paper presents a method to determine the number of visible layers in the outer retina and perform segmentation. Each layer in the outer retina is represented by a Gaussian function, and multiple models with a different number of layers are used to form the outer retina. Parameters of competing models are calculated by using maximum likelihood estimation after which the model that best describes the data is selected. Model selection is based on the goodness of fit and model complexity thereby ensuring that the model that best represents the data is chosen. The method was applied to in-vivo macular images of human retinas acquired by optical coherence tomography after conversion to attenuation coefficients. Examples of detected number of visible layers and corresponding segmentation results are shown in both normal and retinitis pigmentosa affected retinas.
Starting Page	5646
Ending Page	5649
File Size	2902844
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319673
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Retina Data models Complexity theory Image segmentation Maximum likelihood estimation Mathematical model Biomedical optical imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A statistical model of retinal optical coherence tomography image data

Automatic Image Processing Algorithm to Detect Hard Exudates based on Mixture Models

Automatic glaucoma diagnosis from fundus image

Self-assessment for optic disc segmentation

Mixture model-based approach for optic cup segmentation

An improved gradient vector flow algorithm for optic disc segmentation

Support system for the preventive diagnosis of Hypertensive Retinopathy

Maximum likelihood estimation for Rician distributed data in analytical q-ball imaging

Model-based optic nerve head segmentation on retinal fundus images

Method for segmentation of the layers in the outer retina

Similar Documents

A statistical model of retinal optical coherence tomography image data

Automatic Image Processing Algorithm to Detect Hard Exudates based on Mixture Models

Automatic glaucoma diagnosis from fundus image

Self-assessment for optic disc segmentation

Mixture model-based approach for optic cup segmentation

An improved gradient vector flow algorithm for optic disc segmentation

Support system for the preventive diagnosis of Hypertensive Retinopathy

Maximum likelihood estimation for Rician distributed data in analytical q-ball imaging

Model-based optic nerve head segmentation on retinal fundus images

Method for segmentation of the layers in the outer retina