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...
| Main Authors: | , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/42183/ http://irep.iium.edu.my/42183/4/su.pdf |
| _version_ | 1848782232721817600 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-14T16:02:11Z |
| format | Proceeding Paper |
| id | iium-42183 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:02:11Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | 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 Proceeding Paper 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 |
| 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 |
| title | 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_short | Supervised vessel segmentation with minimal features |
| title_sort | supervised vessel segmentation with minimal features |
| topic | RE Ophthalmology TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering |
| url | http://irep.iium.edu.my/42183/ http://irep.iium.edu.my/42183/ http://irep.iium.edu.my/42183/4/su.pdf |