| Project/Area Number |
14580419
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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 |
Intelligent informatics
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| Research Institution | Toyohashi University of Technology |
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Principal Investigator |
YAMAMOTO Shinji Toyohashi University of Technology, Faculty of Engineering, Professor, 工学部, 教授 (80230556)
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| Co-Investigator(Kenkyū-buntansha) |
MIZUNO Shinji 豊橋技術科学大学, 工学部, 助手 (20314099)
滝沢 穂高 豊橋技術科学大学, 工学部, 助手 (40303705)
HOTAKA Takizawa
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2003: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2002: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | Lung Cancer / Image Processing / Pattern Recognition / Computer Aided Diagnosis / CT Image |
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| Research Abstract |
We proposed to develop an automatic screening system for lung cancer detection utilizing CT scanner images. The most important subject to perform this purpose is that the detection accuracy of lung nodules by computer should be realized more than 10 times higher level of present technical status.
In this research activity, we developed the following three different algorithms for feature ectraction and recognition, and realized efficient detection accuracy by combining these three algorithms.
(1)Development of a classification algorithm between abnormal nodules and normal vessels based on 3-D artificial structure models. Abnormal nodule models are produced as 3D sphere with various kinds of radii. Normal vessel models are produced as the combination of two or three cylinders with various kinds of connecting angles and radii. Input image is compared with all of these models and best fitting model is selected which correspond to final decision (abnormal or normal).
(2)Development of a classification algorithm based on principal component analysis method (PCA) of CT intensity information in the region of interest(ROI).
(3)Development of a classification algorithm based on heuristic feature extraction and statistical discriminant function.
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