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Automatic Analysis of Cephalogram for Orthodontics

Research Project

Project/Area Number 07680948
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research Field Biomedical engineering/Biological material science
Research InstitutionKYUSHU INSTITUTE OF TECHNOLOGY

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)

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)
Project Period (FY) 1995 – 1997
Project Status Completed (Fiscal Year 1997)
Budget Amount *help
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1997: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1996: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1995: ¥900,000 (Direct Cost: ¥900,000)
KeywordsOrthodontic Treatment / Cephalogram / Cephalo Analysis / Fuzzy Inference / Neural Network / Neo-Fuzzy Neuron / Fuzzy Clustering / Fuzzy Template Matching / ファジィクラスリング / ファジイ推論
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.

Report

(4 results)
  • 1997 Annual Research Report   Final Research Report Summary
  • 1996 Annual Research Report
  • 1995 Annual Research Report
  • Research Products

    (16 results)

All Other

All Publications (16 results)

  • [Publications] Eiji Uchino: "System Modeling by a Neo-Fuzzy-Neuron with Applications to Acoustic and Chaotic Systems" International Journal on Artificial Intelligence Tools. Vol.4,Nos.1&2. 73-91 (1995)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] 山川 烈: "セファロ画像における重み付き類似性測度を用いた計測点の抽出" Journal of Biomedical Fuzzy and Human Sciences. Vol.2,No.1. 93-101 (1996)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] Eiji Uchino: "High Speed Fuzzy Learning Machine with Guarantee of Global Minimum and Its Application to Chaotic System Identification and Medical Image Processing" International Journal on Artificial Intelligence Tools. Vol.5,Nos.1&2. 23-39 (1996)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] Takeshi Yamakawa: "Neo-Fuzzy-Neuron and Its Learning Algorithms with Applications to the Modeling of Nonlinear Dynamical Systems" “Applications of Fuzzy Logic:Towards High Machine Intelligence Quotient Systems"eds.M.Jamshidi et al., Prentice Hall. 223-243 (1997)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] Eiji Uchino and Takeshi Yamakawa: "System Modeling by a Neo-Fuzzy-Neuron with Applications to Acoustic and Chaotic Systems" International Journal on Artificial Intelligence Tools. Vol.4, Nos.1&2. 73-91 (1995)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] Takeshi Yamakawa, Eiji Uchino, and Masako Morishita: "Extraction of Landmarks in Roentgenographic Cephalogram by Using Weighted Similarity Measure" Journal of Biomedical Fuzzy and Human Sciences. Vol.2, No.1. 93-101 (1996)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] Eiji Uchino and Takeshi Yamakawa: "High Speed Fuzzy Learning Machine with Guarantee of Global Minimum and Its Applications to Chaotic System Identification and Medical Image Processing" International Journal on Artificial Intelligence Tools. Vol.5, Nos.1&2. 23-39 (1996)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] Takeshi Yamakawa and Eiji Uchino: "Neo-Fuzzy-Neuron and Its Learning Algorithms with Applications to the Modeling of Nonlinear Dynamical Systems" in "Applications of Fuzzy Logic : Towards High MachineIntelligence Quotient Systems" eds.M.Jamshidi, A.Titli, L.Zadeh, and S.Boverie, Prentice Hall, New Jersey, USA. 223-243 (1997)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1997 Final Research Report Summary
  • [Publications] 内野 英治: "顎矯正手術前後の硬組織と軟組織移動量のファジィ学習を用いた相互予測" バイオメディカル・ファジィ・システム学会10周年記念大会講演論文集. 119-120 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] Eiji Uchino: "Nonlinear Modeling and Filtering by RBF Network with Application to Noisy Signal" Journal of Information Sciences. Vol.101. 177-185 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] Eiji Uchino: "High Speed Fuzzy Learning Machine with Guarantee of Global Minimum and Its Applications to Chaotic System Identification and Medical Image Processing" International Journal on Artificial Intelligence Tools. Vol.5,Nos.1&2. 23-39 (1996)

    • Related Report
      1996 Annual Research Report
  • [Publications] 山川烈: "セファロ画像における重み付き類似性測度を用いた計測点の抽出" Biomedical Fuzzy and Human Science. Vol.2,No.1. 93-101 (1996)

    • Related Report
      1996 Annual Research Report
  • [Publications] 森下雅子: "トレース線図形を用いない直接的な計測点抽出法" 第55回日本矯正歯科学会大会抄録集. 153-153 (1996)

    • Related Report
      1996 Annual Research Report
  • [Publications] Eiji Uchino: "High Speed Fuzzy Learning Machine with Guarantee of Global Minimum and Its Applications to Chaotic System Identification and Medical Image Processing" International Journal on Artificial Intelligence Tools. to be published (1996)

    • Related Report
      1995 Annual Research Report
  • [Publications] 山川 烈: "セファロ原画像から直接的に計測項目を算定する自動セファロ分析システム" バイオメディカル・ファジィ・システム学会第8回年次大会抄録集. 27-28 (1995)

    • Related Report
      1995 Annual Research Report
  • [Publications] 山川 烈: "セファロ濃淡画像特有の画像処理技術の必要性について" バイオメディカル・ファジィ・システム学会1995年9月シンポジウム論文集. 9-10 (1995)

    • Related Report
      1995 Annual Research Report

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Published: 1995-04-01   Modified: 2016-04-21  

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