2000 Fiscal Year Final Research Report Summary
Automated diagnosis system for dental diseases using functions of the humanvisual system
| Project/Area Number |
11671870
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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 |
病態科学系歯学(含放射線系歯学)
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
KAWAZU Toshiyuki Kyushu Dental Hospital, KYUSHU UNIVERSITY, Research associate, 歯学部・附属病院, 助手 (20294960)
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| Co-Investigator(Kenkyū-buntansha) |
TOYOFUKU Fukai School of Health Sciences, KYUSHU UNIVERSITY, Prof., 医療技術短期大学部, 教授 (10117179)
TOKUMORI Kenji Graduate School of dental Science, KYUSHU UNIVERSITY, Research associate, 大学院・歯学研究院, 助手 (40253463)
TANAKA Takemasa Kyushu Dental Hospital, KYUSHU UNIVERSITY, Research associate, 歯学部・附属病院, 助手 (30163538)
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| Project Period (FY) |
1999 – 2000
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| Keywords | Dental Digital Radiography / Computer-Aided Diagnosis |
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| Research Abstract |
The purpose of this project was to obtain the function of the human visual system from the average observer and apply it to the proximal caries diagnosis as a semi-automated system.
1.Effects of the human visual system on the proximal caries diagnosis
Two types of recording media-the analogue film and the digital imaging films- were used for this experiment. The materials consisted of 27 third molar teeth extracted from young adolescents.Using these equipments and materials, a total of 594 digital images and 189 film radiographs were obtained. Seven oral radiologists evaluated one series at one time. Receiver operating characteristic (ROC) curves were obtained at each exposure of the recording system.
In this experiment, it was concluded that radiation contrast registered by the recording medium determines the information required for the caries diagnosis. The effect of human visual system on the caries diagnosis was minimum.
2.Semi-automated system for the proximal caries diagnosis
Using the results from the above experiment, we have developed the semi-automated system for the proximal caries diagnosis.
In this system, the algorithm for the caries diagnosis is following :
1) extraction of the contour of the proximal surface
2) gray level measurement along the horizontal axis
3) gray level profile along the proximal surface
4) polynomial curve fitting to the profile
5) extraction of the parameters from the function
6) discrimination function for the caries using the above parameters
Diagnostic accuracy obtained from the semi-automated system was comparable to that of the observers. Especially, it was most effective in the system using automated exposure compensation program.
Conclusion
The present semi-automated system may be useful for the proximal caries diagnosis, especially in the system with automated exposure compensation.
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