| Project/Area Number |
63870043
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| Research Category |
Grant-in-Aid for Developmental Scientific Research
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| Allocation Type | Single-year Grants |
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| Research Field |
Radiation science
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| Research Institution | Tokyo University of Agriculture & Technology |
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Principal Investigator |
KOBATAKE Hidefumi Tokyo Univ. of Agri. & Tech., Faculty of Technology Professor, 工学部, 教授 (80013720)
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| Co-Investigator(Kenkyū-buntansha) |
SHIDA Hisao Rosai Hospital for Silicosis, Division of Radiology Department Manager, 放射線科, 部長
TATENO Yukio National Institute of Radiological Sciences, Clinical Research Div. Department M, 臨床研究部, 部長
HOSODA Yutaka Radiation Effects Research Found., Clinical Research Division, Department Manage, 臨床研究部, 部長
NOBECHI Tokuro St. Luke's International Hospital, Advisor, 顧問
大森 隆司 東京農工大学, 工学部, 助教授 (50143384)
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| Project Period (FY) |
1988 – 1989
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| Project Status |
Completed (Fiscal Year 1989)
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| Budget Amount *help |
¥17,300,000 (Direct Cost: ¥17,300,000)
Fiscal Year 1989: ¥4,600,000 (Direct Cost: ¥4,600,000)
Fiscal Year 1988: ¥12,700,000 (Direct Cost: ¥12,700,000)
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| Keywords | Pneumoconiosis / Computed radiography / Automatic diagnosis / Texture analysis / Image processing / Standard film / Computer diagnosis / テクスチャ解析 / パターン認識 |
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| Research Abstract |
The advent of increased government involvement in occupational health maintenance is requiring new approaches to medical decision making. The automatic mass diagnostic screening of medical films for the detection of a specific abnormality could one of promising approaches.
This project is to develop an automatic diagnostic system of pneumoconiosis which is based on the Computed Radiography (CR). CR system is a new X-ray imaging system which produce high quality x-ray images and its equality makes it easy to develop stable automatic diagnostic system.
The summary of the project is as follows: (1) More than 70 CR Tilmis of pneumoconiosis have been collected. Expert readers evaluated the profusion of opacities of pneumoconiosis of these files. From these results, pneumoconiosis CR file data base has been constructed. (2) Five fundamental methods for evaluating profusion of opacities have been developed. The correct classification rates of them ranges from 70 to 8 percent for normal/abnormal classification. (3) Synthetic diagnostic system has been developed, which is based on the methods mentioned above. Its correct classification rate for normal/abnormal decision is 90 percent. This score is obtained from partial region of chest x-ray images and then we can expect that the overall decision will be much higher.
These results show than the synthetic system is promising for the realization of automatic diagnostic system. We will continue to improve each method and try to attain much higher correct decision rate.
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