| Project/Area Number |
12680358
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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 |
計算機科学
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| Research Institution | Iwate Medical University |
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Principal Investigator |
TAKAHASHI Kei Iwate Medical University, School of Liberal Arts and Sciences. Assistant Professor, 教養部, 助教授 (60128923)
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| Co-Investigator(Kenkyū-buntansha) |
OHTAWARA Yasunari Iwate Medical University, Department of Medicine, Assistant, 医学部, 助手 (20285597)
MIURA Yasuhide Iwate University, Faculty of Humanities and Social Sciences Professor, 人文社会科学部, 教授 (20091647)
YONEZAWA Hisashi Iwate Medical University, Department of Medicine, Instructor, 医学部, 講師 (20240377)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2002: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2001: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2000: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | segmentation / brain image / database / MRI / ゼグメンテーション / アルゴリズム |
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| Research Abstract |
The objective of our research project was to find efficient medical image processing algorithms for the practical use of image diagnoses and neuroclinical researches. In the previous study, we developed a PC-based brain image database system, and applied it to neuro-clinical research for localizing the spatial distribution of pyramidal nerve fibers in the pons. In this research, we improved the system by adding the direct image datasets from the newly installed MRI equipment with high magnetic field of 3.0 Tesla, and implementing a neural-network-based algorithm for segmentation of small anatomical structures in th brain. Each patient data consisted of image datasets and numerical information. The direct image datasets could be acquired on line with connection to the imagine equipments via local area network.
Novel 3-D algorithms for MR-MR, MR-PET, PET-PET, and CT-PET registration were introduced. Efficient algorithms for detecting brain surfaces and extracting sulci and gyri based on artificial neural network were implemented to reconstruct precise brain images. All these steps were of segmentation procedures could be automatically carried out. Our next goal is to automatically segment the cerebellum, the hippocampus, and the corpus callosum.
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