Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine

DOI

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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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  - 10.2991/cmsa-18.2018.51
ID  - Li2018/04
ER  -