2006 Fiscal Year Final Research Report Summary
Development of computer-aided diagnosis system for autopsy imaging
| Project/Area Number |
17360190
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
KOBATAKE Hidefumi Tokyo University of Agriculture and Technology, Headquarters, President, 本部, 学長 (80013720)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMIZU Akinobu Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Associate Professor, 大学院共生科学技術研究院, 助教授 (80262880)
EZAWA Hidefumi National Institute of Radiological Sciences, National Institute of Radiological Sciences, chief pathologist, 重粒子医科学センター病院, 医長 (20300907)
IWASE Hirotaro Chiba Univ., Graduate School of Medicine, Professor, 大学院医学研究院, 教授 (30272420)
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| Project Period (FY) |
2005 – 2006
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| Keywords | Autopsy Imaging / Image Processing / Computer-aided Diagnosis / CT / Bone Fracture |
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| Research Abstract |
First, we established an interface system for transferring the data from the CT scanner in Chiba University to personal computer for the image processing and built an image database for this study. Then we focused on head and thorax in CT images to develop an bone fracture detection system since bone fractures are one of the most important cause of death Followings are main achievements of this study.
1. Head
The proposed algorithm detected a symmetric plane of the head in 3D CT image and subtracted the gray values in the left part and their corresponding values in the right parts to enhance the difference. The variation of the subtracted values were model statistically using eigen-image method We proposed and evaluated the bone fracture detection method which is based on the results of the analysis.
2. Thorax
We developed the following procedure for detecting bone fracture in the thorax CT images
a) extraction of bone area
b) classification of bones based on human anatomy
c) detection of bone fracture
In the process a), we proposed not only the binarization based method but also fine texture based method. We have applied the process to the CT images in the database and confirmed that it could extract the bone region successfully. The process b) has the statistical model of the point set which can classify the bones into anatomical elements. From the experimental results, it was found that the success rate of the anatomical labeling was about 70%. Finally the last process detected locations of bone fracture based on the continuity of the bone surface. The experimental results showed that it could detect about 70% of the bone fractures with some false positives.
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