Retina fundus image mask generation using pseudo parametric modeling technique

Retina fundus image mask generation using pseudo parametric modeling technique

This paper discusses a new pseudo modeling technique for the generation of retina fundus image (RFI) mask. The model coefficients necessary for the generation of the mask has been estimated from the synaptic weights of real-valued neural network. Performance analysis of the newly proposed three st...

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Bibliographic Details
Main Authors: Aibinu, Abiodun Musa, Salami, Momoh Jimoh Emiyoka, Shafie, Amir Akramin
Format: Article
Language:English
Published: IIUM Press 2010
Subjects:
TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:http://irep.iium.edu.my/1774/
http://irep.iium.edu.my/1774/
http://irep.iium.edu.my/1774/1/FUNDUS_MASK.pdf
id iium-1774
recordtype eprints
spelling iium-17742012-03-10T12:31:12Z http://irep.iium.edu.my/1774/ Retina fundus image mask generation using pseudo parametric modeling technique Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Shafie, Amir Akramin TK5101 Telecommunication. Including telegraphy, radio, radar, television This paper discusses a new pseudo modeling technique for the generation of retina fundus image (RFI) mask. The model coefficients necessary for the generation of the mask has been estimated from the synaptic weights of real-valued neural network. Performance analysis of the newly proposed three step technique has been evaluated using DRIVE databases and other RFI obtained from other sources. he accuracy obtained by the application of the proposed technique on RFI contained in the DRIVE database varies between 99.62% and 99.97%. IIUM Press 2010 Article PeerReviewed application/pdf en http://irep.iium.edu.my/1774/1/FUNDUS_MASK.pdf Aibinu, Abiodun Musa and Salami, Momoh Jimoh Emiyoka and Shafie, Amir Akramin (2010) Retina fundus image mask generation using pseudo parametric modeling technique. IIUM Engineering Journal, 11 (2). pp. 163-177. ISSN 1511-788X http://www.iium.edu.my/ejournal/home2010/index.php/iiumej/article/view/31
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle TK5101 Telecommunication. Including telegraphy, radio, radar, television
Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
Shafie, Amir Akramin
Retina fundus image mask generation using pseudo parametric modeling technique
description This paper discusses a new pseudo modeling technique for the generation of retina fundus image (RFI) mask. The model coefficients necessary for the generation of the mask has been estimated from the synaptic weights of real-valued neural network. Performance analysis of the newly proposed three step technique has been evaluated using DRIVE databases and other RFI obtained from other sources. he accuracy obtained by the application of the proposed technique on RFI contained in the DRIVE database varies between 99.62% and 99.97%.
format Article
author Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
Shafie, Amir Akramin
author_facet Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
Shafie, Amir Akramin
author_sort Aibinu, Abiodun Musa
title Retina fundus image mask generation using pseudo parametric modeling technique
title_short Retina fundus image mask generation using pseudo parametric modeling technique
title_full Retina fundus image mask generation using pseudo parametric modeling technique
title_fullStr Retina fundus image mask generation using pseudo parametric modeling technique
title_full_unstemmed Retina fundus image mask generation using pseudo parametric modeling technique
title_sort retina fundus image mask generation using pseudo parametric modeling technique
publisher IIUM Press
publishDate 2010
url http://irep.iium.edu.my/1774/
http://irep.iium.edu.my/1774/
http://irep.iium.edu.my/1774/1/FUNDUS_MASK.pdf
first_indexed 2023-09-18T20:09:16Z
last_indexed 2023-09-18T20:09:16Z
_version_ 1777407368240300032

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