Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features
This paper presents an algorithm for tracking the torso of the user in a computer-aided breast self-examination system. The algorithm uses a neural network-based skin classifier for segmenting the skin area from the non-skin area. Using the skin mask produced by the classifier, the contours of the b...
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oai:animorepository.dlsu.edu.ph:faculty_research-28912021-07-29T08:06:05Z Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features Masilang, Rey Anthony A. Cabatuan, Melvin K. Dadios, Elmer P. Gan Lim, Laurence This paper presents an algorithm for tracking the torso of the user in a computer-aided breast self-examination system. The algorithm uses a neural network-based skin classifier for segmenting the skin area from the non-skin area. Using the skin mask produced by the classifier, the contours of the body are extracted and used to identify the region containing the torso of the user. The algorithm is tested on 4 different videos. The performance of the algorithm is measured in terms of its F1-score. Results show that the algorithm is capable of accurate tracking with an F1-score of 92.97%. © 2014 IEEE. 2015-01-26T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/1892 Faculty Research Work Animo Repository Breast—Examination Neural networks (Computer science) Electrical and Computer Engineering Electrical and Electronics |
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Breast—Examination Neural networks (Computer science) Electrical and Computer Engineering Electrical and Electronics |
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Breast—Examination Neural networks (Computer science) Electrical and Computer Engineering Electrical and Electronics Masilang, Rey Anthony A. Cabatuan, Melvin K. Dadios, Elmer P. Gan Lim, Laurence Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features |
| description |
This paper presents an algorithm for tracking the torso of the user in a computer-aided breast self-examination system. The algorithm uses a neural network-based skin classifier for segmenting the skin area from the non-skin area. Using the skin mask produced by the classifier, the contours of the body are extracted and used to identify the region containing the torso of the user. The algorithm is tested on 4 different videos. The performance of the algorithm is measured in terms of its F1-score. Results show that the algorithm is capable of accurate tracking with an F1-score of 92.97%. © 2014 IEEE. |
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text |
| author |
Masilang, Rey Anthony A. Cabatuan, Melvin K. Dadios, Elmer P. Gan Lim, Laurence |
| author_facet |
Masilang, Rey Anthony A. Cabatuan, Melvin K. Dadios, Elmer P. Gan Lim, Laurence |
| author_sort |
Masilang, Rey Anthony A. |
| title |
Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features |
| title_short |
Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features |
| title_full |
Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features |
| title_fullStr |
Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features |
| title_full_unstemmed |
Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features |
| title_sort |
computer-aided bse torso tracking algorithm using neural networks, contours, and edge features |
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Animo Repository |
| publishDate |
2015 |
| url |
https://animorepository.dlsu.edu.ph/faculty_research/1892 |
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