Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms

Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms

This paper focuses on breast self-examination (BSE) stroke position and palpation level classification for the development of a computer vision-based BSE training and guidance system. In this study, image frames are extracted from a BSE video and processed considering the color information, shape, a...

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Bibliographic Details
Main Authors: Cabatuan, Melvin K., Dadios, Elmer Jose P., Naguib, Raouf N. G., Oikonomou, Andreas
Format: text
Published: Animo Repository 2012
Subjects:
Breast—Examination
Self-examination, Medical
Computer vision in medicine
Electrical and Computer Engineering
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/563
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Institution: De La Salle University
Description
Summary:This paper focuses on breast self-examination (BSE) stroke position and palpation level classification for the development of a computer vision-based BSE training and guidance system. In this study, image frames are extracted from a BSE video and processed considering the color information, shape, and texture by wavelet transform and first order color moment. The new approach using artificial neural network and wavelet transform can identify BSE stroke positions and palpation levels, i.e. light, medium, and deep, at 97.8 % and 87.5 % accuracy respectively. © 2012 IEEE.

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