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Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the re...

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Main Authors: Rahman, H.A., Harun, Sulaiman Wadi, Arof, Hamzah, Irawati, Ninik, Musirin, I., Ibrahim, Fatimah, Ahmad, Harith
Format: Article
Published: International Society for Optical Engineering (SPIE) 2014
Subjects:
QC Physics
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/14328/
https://doi.org/10.1117/1.JBO.19.5.057009
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spelling my.um.eprints.143282018-10-09T04:45:09Z http://eprints.um.edu.my/14328/ Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor Rahman, H.A. Harun, Sulaiman Wadi Arof, Hamzah Irawati, Ninik Musirin, I. Ibrahim, Fatimah Ahmad, Harith QC Physics TK Electrical engineering. Electronics Nuclear engineering An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems. International Society for Optical Engineering (SPIE) 2014-05-19 Article PeerReviewed Rahman, H.A. and Harun, Sulaiman Wadi and Arof, Hamzah and Irawati, Ninik and Musirin, I. and Ibrahim, Fatimah and Ahmad, Harith (2014) Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor. Journal of Biomedical Optics (JBO), 19 (5). 057009. ISSN 1083-3668 https://doi.org/10.1117/1.JBO.19.5.057009 doi:10.1117/1.JBO.19.5.057009
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QC Physics
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QC Physics
TK Electrical engineering. Electronics Nuclear engineering
Rahman, H.A.
Harun, Sulaiman Wadi
Arof, Hamzah
Irawati, Ninik
Musirin, I.
Ibrahim, Fatimah
Ahmad, Harith
Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
description An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.
format Article
author Rahman, H.A.
Harun, Sulaiman Wadi
Arof, Hamzah
Irawati, Ninik
Musirin, I.
Ibrahim, Fatimah
Ahmad, Harith
author_facet Rahman, H.A.
Harun, Sulaiman Wadi
Arof, Hamzah
Irawati, Ninik
Musirin, I.
Ibrahim, Fatimah
Ahmad, Harith
author_sort Rahman, H.A.
title Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
title_short Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
title_full Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
title_fullStr Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
title_full_unstemmed Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
title_sort classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
publisher International Society for Optical Engineering (SPIE)
publishDate 2014
url http://eprints.um.edu.my/14328/
https://doi.org/10.1117/1.JBO.19.5.057009
_version_ 1643689783886610432
score 13.211869

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