Supervised vessel segmentation with minimal features

Che Azemin, Mohd Zulfaezal and Mohd Tamrin, Mohd Izzuddin (2014) Supervised vessel segmentation with minimal features. In: IEEE 19th Functional Electrical Stimulation Society Annual Conference (IFESS), 17-19 Sep 2014, Kuala Lumpur.

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Abstract

Current state-of-the art supervised vessel segmentation methods require large number of feature vectors to construct a good model. In this paper, we propose a framework to optimally search for optimal features as inputs to Artificial Neural Network (ANN) trained by Scaled Conjugate Gradient (SCG). SCG is known to speed-up the learning stage in a supervised learning especially when error reduction is critical. The proposed framework is able to reduce features from 16 to 4 dimensions and the overall performance is only decreased by 1% in average

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	6768/42183 "2014 IEEE 19TH INTERNATIONAL FUNCTIONAL ELECTRICAL STIMULATION SOCIETY ANNUAL CONFERENCE (IFESS) ISBN: 9781479964833
Uncontrolled Keywords:	Vessel Segmentation, Feature Selection, Artificial Neural Network, Scaled Conjugate Gradient Backpropation
Subjects:	R Medicine > RE Ophthalmology T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Allied Health Sciences > Department of Optometry and Visual Science Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Mohd Izzuddin Mohd Tamrin
Date Deposited:	02 Mar 2015 09:32
Last Modified:	16 Oct 2015 15:00
URI:	http://irep.iium.edu.my/id/eprint/42183

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