Supervised vessel segmentation with minimal features
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). S...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2014
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| Online Access: | http://irep.iium.edu.my/42183/ http://irep.iium.edu.my/42183/ http://irep.iium.edu.my/42183/4/su.pdf |
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iium-42183 |
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eprints |
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iium-421832015-10-16T07:00:53Z http://irep.iium.edu.my/42183/ Supervised vessel segmentation with minimal features Che Azemin, Mohd Zulfaezal Mohd Tamrin, Mohd Izzuddin RE Ophthalmology TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering 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 2014 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/42183/4/su.pdf 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. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7036744 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
RE Ophthalmology TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering |
| spellingShingle |
RE Ophthalmology TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering Che Azemin, Mohd Zulfaezal Mohd Tamrin, Mohd Izzuddin Supervised vessel segmentation with minimal features |
| description |
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 |
| format |
Conference or Workshop Item |
| author |
Che Azemin, Mohd Zulfaezal Mohd Tamrin, Mohd Izzuddin |
| author_facet |
Che Azemin, Mohd Zulfaezal Mohd Tamrin, Mohd Izzuddin |
| author_sort |
Che Azemin, Mohd Zulfaezal |
| title |
Supervised vessel segmentation with minimal features |
| title_short |
Supervised vessel segmentation with minimal features |
| title_full |
Supervised vessel segmentation with minimal features |
| title_fullStr |
Supervised vessel segmentation with minimal features |
| title_full_unstemmed |
Supervised vessel segmentation with minimal features |
| title_sort |
supervised vessel segmentation with minimal features |
| publishDate |
2014 |
| url |
http://irep.iium.edu.my/42183/ http://irep.iium.edu.my/42183/ http://irep.iium.edu.my/42183/4/su.pdf |
| first_indexed |
2023-09-18T21:00:11Z |
| last_indexed |
2023-09-18T21:00:11Z |
| _version_ |
1777410571712331776 |