Monocular depth level estimation for breast self-examination (BSE) using RGBD BSE dataset

Authors

John Anthony C. Jose, De La Salle University, Manila
Melvin K. Cabatuan, De La Salle University, Manila
Robert Kerwin Billones, De La Salle University, Manila
Elmer P. Dadios, De La Salle University, Manila
Laurence A. 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

2016-January

Publication Date

1-5-2016

Abstract

Up until now, there had been no existing literature in depth level estimation algorithm for BSE using a simple camera that provides quantitative accuracy. They can only show their effectiveness thru graphs. In this paper, we present the RGBD BSE dataset and a depth level quantization scheme that provides an avenue for training a Machine learning model and calculating its hit rate. We were able to show that the previous study's accuracy is 30.33%. Moreover, adding a simple shadow area as feature and changing the Machine Learning prediction model to Support Vector Machine boosts the algorithm's accuracy to 58.83%. © 2015 IEEE.

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Digitial Object Identifier (DOI)

10.1109/TENCON.2015.7372948

Recommended Citation

Jose, J. C., Cabatuan, M. K., Billones, R., Dadios, E. P., & Gan Lim, L. A. (2016). Monocular depth level estimation for breast self-examination (BSE) using RGBD BSE dataset. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2016-January https://doi.org/10.1109/TENCON.2015.7372948

Disciplines

Biomedical | Electrical and Computer Engineering

Keywords

Breast—Examination; Self-examination, Medical; Computer vision in medicine; Depth perception

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