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JACIII Vol.18 No.3 pp. 253-261
doi: 10.20965/jaciii.2014.p0253
(2014)

Paper:

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Dental Numbering for Periapical Radiograph Based on Multiple Fuzzy Attribute Approach

Martin Leonard Tangel*, Chastine Fatichah**, Fei Yan*,
Janet Pomares Betancourt*, Muhammad Rahmat Widyanto***,
Fangyan Dong*, and Kaoru Hirota*

*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**Department of Informatics, Institut Teknologi Sepuluh Nopember, Kampus ITS Keputih, Surabaya 60111, Indonesia

***Department of Computer Science, University of Indonesia, Depok Campus, Depok 16424, West Java, Indonesia

Received:
September 24, 2013
Accepted:
February 17, 2014
Published:
May 20, 2014
Creative Commons License
Keywords:
dental numbering, dental classification, fuzzy inference, personal identification, periapical radiograph
Abstract
The dental numbering for periapical radiograph based on multiple fuzzy attribute approach proposed here analyzes each individual tooth based on multiple criteria such as area/perimeter and width/height ratios. The classification and numbering in a special dental image called a periapical radiograph is studied without speculative classification in cases of ambiguous objects, so an accurate, assistive result is obtained due to the capability of handling ambiguous teeth. Experiment results in using periapical dental radiograph from the University of Indonesia indicate a total classification accuracy of 82.51%, an average classification rate per input radiograph of 84.29%, a maxilla-mandible identification accuracy from 78 radiographs of 82.05%, and a numbering accuracy from 15 radiographs of 90.47%. It is planned that the proposed classification and numbering be implemented as a submodule for dental-based personal identification now being developed.
Cite this article as:
M. Tangel, C. Fatichah, F. Yan, J. Betancourt, M. Widyanto, F. Dong, and K. Hirota, “Dental Numbering for Periapical Radiograph Based on Multiple Fuzzy Attribute Approach,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.3, pp. 253-261, 2014.
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