Development of intelligent image database system for medical x-ray images
| Project/Area Number |
63880008
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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 |
Informatics
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| Research Institution | Nagoya University |
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Principal Investigator |
TORIWAKI Jun-ichiro Nagoya University, Faculty of Eng. Prof., 工学部, 教授 (30023138)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUMOTO Tooru Research Institute of Radiological Medicine Chief Researcher, 主任研究官
HASEGAWA Junichi Chukyo Univ. Dep. of General Arts, Prof., 教養部, 教授 (30126891)
SUZUKI Hidetomo Nagoya Univ. Faculty of Eng. Lecturer, 工学部, 講師 (20158976)
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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 |
¥5,200,000 (Direct Cost: ¥5,200,000)
Fiscal Year 1989: ¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 1988: ¥2,500,000 (Direct Cost: ¥2,500,000)
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| Keywords | Chest x-ray image / image database / sketch / image retrieval / intelligent retrieval / image understanding / medical images / 画像データベース |
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| Research Abstract |
In this research, we tried to develope a highly intelligent image database for medical x-ray images , by integrating image understanding computer-aided diagnosis, and image retrieval.
(1)Transferring the past system : We already developed a unique database for chest x-ray images which has the ability to manipulate sketches of the images stored in the database. By this system we can retrieve desired images by using pictorial keys given on a sketch and <query-by-pictorial-example> schemes. Since this system was implemented on a conventional large scale computer, we first transferred this system to the EWS(SUN-4) , and added the new function to write down diagnostic records into an original x-ray image.
(2)Computer-aided diagnosis of cancer by x-ray images : We studied comuter diagnosis of lung and stomach cancer by applying image understanding techniques. Concerning stomach cancer we developed a procedure to detect small regions which are suspected to the abnormal from a double-contrast stomach x-ray image. Automated screening system for a photofluorogram was tested its performance to diagnose a 100mm films now used for mass screening and its limitations and basic policy for future improvement were made clear. For quantitative diagnosis of pneumoconiosis, we developed a procedure to automatically classify diseases into normal and nine classes according to the criteria given by ILO by detecting small rounded opacities observed in the lung field of pneumoconiosis x-ray images.
(3)Supporting software tool : In order to support development of computer-aided image diagnosis system, we improved the image processing expert system IMPRESS we had previously developed , and began to study the intelligent image interface. Concerning the former, we added a goal transformation function to the system IMPRESS.
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Report
(3 results)
Research Products
(13 results)
