1997 Fiscal Year Final Research Report Summary
Automatic Analysis of Cephalogram for Orthodontics
| Project/Area Number |
07680948
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | KYUSHU INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
UCHINO Eiji Kyushu Institute of Technology, Department of Control Engineering and Science, Associate Professor, 情報工学部, 助教授 (30168710)
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| Co-Investigator(Kenkyū-buntansha) |
MIKI Tsutomu Kyushu Institute of Technology, Department of Control Engineering and Science, R, 情報工学部, 助手 (20231607)
YAMAKAWA Takeshi Kyushu Institute of Technology, Department of Control Engineering and Science, P, 情報工学部, 教授 (00005547)
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| Project Period (FY) |
1995 – 1997
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| Keywords | Orthodontic Treatment / Cephalogram / Cephalo Analysis / Fuzzy Inference / Neural Network / Neo-Fuzzy Neuron / Fuzzy Clustering / Fuzzy Template Matching |
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| Research Abstract |
(1995)
A neo-fuzzy-neuron, presented by the authors in 1992, was generalized and modified, which we call a generalized fuzzy learning machine. This machine can well grasp the nonlinear correlation of each input and output. It has a very high nonlinear mapping ability compared with the conventional neural network, and it guaranteesa global minimum. Furthermore, the learning speed and its accuracy are improved drastically, It was successfully applied to the automatic detection of landmark positions in the roentgenographic cephalogram for an orthodontic treatment.
(1996)
An extraction of landmarks in a roentgenographic cephalogram by using a neural network and a fuzzy template matching was proposed. Two kinds of weighted similarity measures are newly proposed for a fuzzy template matching. The rough region where a landmark is supposed to be located is first found out by a neural network. The fuzzy template matching is then performed over this region to find the exact location of its landmark. Typical landmarks were successfully found in the actual roentgenographic cephalogram within a permissible error for a practical use.
(1997)
Growth prediction of craniofacial complex by using an RBFN(Radial Basis Function Network) was proposed. The growth prediction of craniofacial complex is very important in the field of orthodontics, because if it is not well predicted re-operation would be necessary, which causes physical and/or mental pain to a patient. A set of learning data was first divided into three skeletal groups by Fuzzy clustering, and then RBFN was constructed for each cluster. The prediction was performed by taking the weighted sum of the outputs of each RBFN.The prediction results were promising.
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