A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been...

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Main Authors: Kaderi, Mohd Arifin, Aibinu, Abiodun Musa, Salami, Momoh Jimoh Emiyoka
Format: Proceeding Paper
Language:English
Published: IEEE 2012
Subjects:
Online Access:http://irep.iium.edu.my/32181/1/A_new_method_of_vascular_point_detection_using.pdf
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author Kaderi, Mohd Arifin
Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
author_facet Kaderi, Mohd Arifin
Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
author_sort Kaderi, Mohd Arifin
collection IIUM
description Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database.
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spelling oai:generic.eprints.org:321812022-06-08T06:38:47Z http://irep.iium.edu.my/32181/ A new method of vascular point detection using artificial neural network Kaderi, Mohd Arifin Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka QM Human anatomy Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database. IEEE 2012-12 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/32181/1/A_new_method_of_vascular_point_detection_using.pdf Kaderi, Mohd Arifin and Aibinu, Abiodun Musa and Salami, Momoh Jimoh Emiyoka (2012) A new method of vascular point detection using artificial neural network. In: IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2012 , 17-19 December 2012, Langkawi, Kedah. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6498142&queryText%3DA+new+method+of+vascular+point+detection+using++Artificial+Neural+Network 978-1-4673-1666-8/12/$31.00 ©2012 IEEE
spellingShingle QM Human anatomy
Kaderi, Mohd Arifin
Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
A new method of vascular point detection using artificial neural network
title A new method of vascular point detection using artificial neural network
title_full A new method of vascular point detection using artificial neural network
title_fullStr A new method of vascular point detection using artificial neural network
title_full_unstemmed A new method of vascular point detection using artificial neural network
title_short A new method of vascular point detection using artificial neural network
title_sort new method of vascular point detection using artificial neural network
topic QM Human anatomy
url http://irep.iium.edu.my/32181/1/A_new_method_of_vascular_point_detection_using.pdf
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