Research Papers: Sensing

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

[+] Author Affiliations
Husna Abdul Rahman

University of Malaya, Department of Electrical Engineering, Faculty of Engineering, Kuala Lumpur 50603, Malaysia

University of Malaya, Photonics Research Centre, Department of Physics, Faculty of Science, Kuala Lumpur 50603, Malaysia

Universiti Teknologi MARA (UiTM), Faculty of Electrical Engineering, Shah Alam 40450, Malaysia

Sulaiman Wadi Harun

University of Malaya, Department of Electrical Engineering, Faculty of Engineering, Kuala Lumpur 50603, Malaysia

University of Malaya, Photonics Research Centre, Department of Physics, Faculty of Science, Kuala Lumpur 50603, Malaysia

Hamzah Arof

University of Malaya, Department of Electrical Engineering, Faculty of Engineering, Kuala Lumpur 50603, Malaysia

Ninik Irawati

University of Malaya, Photonics Research Centre, Department of Physics, Faculty of Science, Kuala Lumpur 50603, Malaysia

Ismail Musirin

Universiti Teknologi MARA (UiTM), Faculty of Electrical Engineering, Shah Alam 40450, Malaysia

Fatimah Ibrahim

University of Malaya, Medical Informatics and Biological Micro-electro-mechanical Systems Specialized Laboratory, Department of Biomedical Engineering, Faculty of Engineering, Kuala Lumpur 50603, Malaysia

Harith Ahmad

University of Malaya, Photonics Research Centre, Department of Physics, Faculty of Science, Kuala Lumpur 50603, Malaysia

J. Biomed. Opt. 19(5), 057009 (May 19, 2014). doi:10.1117/1.JBO.19.5.057009
History: Received January 2, 2014; Revised March 10, 2014; Accepted April 7, 2014
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Abstract.  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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Husna Abdul Rahman ; Sulaiman Wadi Harun ; Hamzah Arof ; Ninik Irawati ; Ismail Musirin, et al.
"Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor", J. Biomed. Opt. 19(5), 057009 (May 19, 2014). ; http://dx.doi.org/10.1117/1.JBO.19.5.057009


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