Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)

Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine

Authors
Zhi Li, Ting Guo, Fangzhou Bao, Rodney Payne
Corresponding Author
Zhi Li
Available Online April 2018.
DOI
https://doi.org/10.2991/cmsa-18.2018.51How to use a DOI?
Keywords
teeth classification; Hu moment invariant; extreme learning machine
Abstract

To improve the computer-assisted diagnosis and decision in dentistry, we tested a new method combining Hu moment invariant (HMI) method and extreme learning machine (ELM) to implement the teeth classification in cross-section image of Cone Beam Computed Tomography (CBCT). 160 images were analyzed and 4 categories were recognized. The results showed the sensitivities of incisors, canine, premolar, and molars were 78.25± 6.02%, 78.00± 5.99%, 79.25± 7.91%, and 78.75± 5.17%, better than ANN statistical-significantly.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
Series
Advances in Intelligent Systems Research
Publication Date
April 2018
ISBN
10.2991/cmsa-18.2018.51
ISSN
1951-6851
DOI
https://doi.org/10.2991/cmsa-18.2018.51How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhi Li
AU  - Ting Guo
AU  - Fangzhou Bao
AU  - Rodney Payne
PY  - 2018/04
DA  - 2018/04
TI  - Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine
BT  - Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
PB  - Atlantis Press
SP  - 220
EP  - 223
SN  - 1951-6851
UR  - https://doi.org/10.2991/cmsa-18.2018.51
DO  - https://doi.org/10.2991/cmsa-18.2018.51
ID  - Li2018/04
ER  -