Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features

Authors

Rey Anthony A. Masilang, De La Salle University, Manila
Melvin K. Cabatuan, De La Salle University, Manila
Elmer P. Dadios, De La Salle University, Manila
Laurence Gan Lim, De La Salle University, Manila

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Volume

2015-January

Publication Date

1-26-2015

Abstract

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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Digitial Object Identifier (DOI)

10.1109/TENCON.2014.7022300

Recommended Citation

Masilang, R. A., Cabatuan, M. K., Dadios, E. P., & Gan Lim, L. (2015). Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2015-January https://doi.org/10.1109/TENCON.2014.7022300

Disciplines

Electrical and Computer Engineering | Electrical and Electronics

Keywords

Breast—Examination; Neural networks (Computer science)

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