| Project/Area Number |
05558033
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| Research Category |
Grant-in-Aid for Developmental Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Toyohashi University of Technology |
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Principal Investigator |
YAMAMOTO Shinji Toyohashi University of Technology, Professor, 工学部, 教授 (80230556)
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| Co-Investigator(Kenkyū-buntansha) |
HAO Jiang Toyohashi University of Technology, Assistant Professor, 工学部, 助手 (00242917)
TAKAGI Hiroshi Hitachi Medical Corpolation, Chief engineer, CT設計部, 主任技師
IINUMA Takeshi Saitama Institute of Technology, Professor, 工学部, 教授 (60159572)
TATENO Yukio National Institute of Radiological Science, 嘱託 (90163493)
MATSUMOTO Mitsuomi Tokyo Metropolitan College of Allied Medical Sciences, Professor, 放射線科, 教授 (20209654)
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| Project Period (FY) |
1993 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥11,100,000 (Direct Cost: ¥11,100,000)
Fiscal Year 1995: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1994: ¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 1993: ¥6,900,000 (Direct Cost: ¥6,900,000)
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| Keywords | Lung cancer / Image prosessing / Pattern recognition / Computer aided diagnosis / CT image |
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| Research Abstract |
This paper reports the image processing technique for computer-aided diagnosis of lung cancer by CT (LSCT). LSCT is the newly developed mobile-type CT scanner for the mass screening of lung cancer by our project team. In this new LSCT system, one essential problem is the increase of image informantion to about 30 slices per person from 1 X-ray film. We tried to reduce the image information drastically to be displayd for the doctor, by image processing techniques, as follows.
NO.1 approach (MIP method)
MIP mehtod (maximum intensity projection)is considered, where the three-dimensional information composed of 30 slices of patient's lung tissue is displayd by a projection on the two-dimensional plane. The most important problem to apply MIP method for LSCT is to delete beforehand the interfering organ signals that could mask the pathological shadows. We developed a new and stable method for it using mathematical morphology, Split-Quoit filter, model information, and active contour model (snake).
This method is applied for 68 patient samples and the result is promissing.
NO.2 approach (Quoit filter method)
A new method is introduced which automatically recognizes the candidate regions for the pathological shadows. By displaying only the slices containing the candidates regions to the doctor, the number of cross sections to bediagnosed can be drastically reduced.
The shadow of the lung cancer which exists in the lung field, appears isolated and with a small area. By utilizing this property, 2-D , 3-D Quoit filters (Q-filters) and MIP-2D-Q filter are developed to extract the isolated shadow automatically, where Q-filters are those based on mathematical morphology.
This method is applied for 68 patient samples including 1809 images, and the number of displayd images to be diagnosed by the doctor is reduced to 144 images, which means the reductin rate 8% (2.1 images/patient). Among those, the 5 cancer patients are extracted correctly.
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