TY - JOUR
T1 - Dental numbering for periapical radiograph based on multiple fuzzy attribute approach
AU - Tangel, Martin Leonard
AU - Fatichah, Chastine
AU - Yan, Fei
AU - Betancourt, Janet Pomares
AU - RahmatWidyanto, Muhammad
AU - Dong, Fangyan
AU - Hirota, Kaoru
PY - 2014/5
Y1 - 2014/5
N2 - 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.
AB - 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.
KW - Dental classification
KW - Dental numbering
KW - Fuzzy inference
KW - Periapical radiograph
KW - Personal identification
UR - http://www.scopus.com/inward/record.url?scp=84901001306&partnerID=8YFLogxK
U2 - 10.20965/jaciii.2014.p0253
DO - 10.20965/jaciii.2014.p0253
M3 - Article
AN - SCOPUS:84901001306
SN - 1343-0130
VL - 18
SP - 253
EP - 261
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
IS - 3
ER -