頭部MR画像における多発性硬化症のコンピュータ支援診断システムの開発
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| Published in | 日本放射線技術学会雑誌 Vol. 70; no. 3; pp. 223 - 229 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
公益社団法人 日本放射線技術学会
2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0369-4305 1881-4883 |
| DOI | 10.6009/jjrt.2014_JSRT_70.3.223 |
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| Author | 日浦, 友樹 石田, 隆行 立永, 謹 川下, 郁生 大倉, 保彦 |
|---|---|
| Author_xml | – sequence: 1 fullname: 川下, 郁生 organization: 広島国際大学保健医療学部診療放射線学科 – sequence: 1 fullname: 立永, 謹 organization: 広島国際大学大学院医療・福祉科学研究科医療工学専攻 – sequence: 1 fullname: 石田, 隆行 organization: 大阪大学大学院医学系研究科保健学専攻 – sequence: 1 fullname: 日浦, 友樹 organization: 独立行政法人労働者健康福祉機構中国労災病院 – sequence: 1 fullname: 大倉, 保彦 organization: 広島国際大学大学院医療・福祉科学研究科医療工学専攻 |
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| Copyright | 2014 公益社団法人 日本放射線技術学会 |
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| DOI | 10.6009/jjrt.2014_JSRT_70.3.223 |
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| Discipline | Medicine |
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| EndPage | 229 |
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| References | 1)難病情報センター．http://www.nanbyou.or.jp/sikkan/068_i.htm(2013/3/30 3)Guttmann CR, Kikinis R, Anderson MC, et al. Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility. J Magn Reson Imaging 1999; 9(4): 509-518. 4)Khayati R, Vafadust M, Towhidkhah F, et al. A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Comput Med Imaging Graph 2008; 32(2): 124-133. 5)Anbeek P, Vincken KL, van Osch MJ, et al. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004; 21(3): 1037-1044. 12)Dougherty RP, Kunzelmann K-H. Computing Local Thickness of 3D Structures with ImageJ. Microscopy & Microanalysis 2007 Meeting 2007. 8)Van Leemput K, Maes F, Vandermeulen D, et al. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 2001; 20(8): 677-688. 9)Yamamoto D, Arimura H, Kakeda S, et al. Development of automated method for detection of multiple sclerosis candidate regions based on brain magnetic resonance images, 電子情報通信学会技術研究報告．MI，医用画像2007; 107(220): 51-52. 7)Raff U, Rojas GM, Hutchinson M, et al. Quantitation of T2 lesion load in patients with multiple sclerosis: a novel semiautomated segmentation technique. Acad Radiol 2000; 7(4): 237-247. 10)Lao Z, Shen D, Liu D, et al. Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad Radiol 2008; 15(3): 300-313. 2)Goldberg-Zimring D, Achiron A, Guttmann CR, et al. Three-dimensional analysis of the geometry of individual multiple sclerosis lesions: detection of shape changes over time using spherical harmonics. J Magn Reson Imaging 2003; 18(3): 291-301. 11)Bradley PS, Fayyad UM. Refining initial points for k-means clustering. Proceedings of the 15th International Conference on Machine Learning. 1998; 91-99. 6)Anbeek P, Vincken KL, van Osch MJ, et al. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 2004; 8(3): 205-215. 13)栗田多喜夫．サポートベクターマシン入門．東京：産業技術総合研究所 脳神経情報研究部門，2002. |
| References_xml | – reference: 10)Lao Z, Shen D, Liu D, et al. Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad Radiol 2008; 15(3): 300-313. – reference: 13)栗田多喜夫．サポートベクターマシン入門．東京：産業技術総合研究所 脳神経情報研究部門，2002. – reference: 2)Goldberg-Zimring D, Achiron A, Guttmann CR, et al. Three-dimensional analysis of the geometry of individual multiple sclerosis lesions: detection of shape changes over time using spherical harmonics. J Magn Reson Imaging 2003; 18(3): 291-301. – reference: 5)Anbeek P, Vincken KL, van Osch MJ, et al. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004; 21(3): 1037-1044. – reference: 7)Raff U, Rojas GM, Hutchinson M, et al. Quantitation of T2 lesion load in patients with multiple sclerosis: a novel semiautomated segmentation technique. Acad Radiol 2000; 7(4): 237-247. – reference: 4)Khayati R, Vafadust M, Towhidkhah F, et al. A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Comput Med Imaging Graph 2008; 32(2): 124-133. – reference: 1)難病情報センター．http://www.nanbyou.or.jp/sikkan/068_i.htm(2013/3/30)． – reference: 8)Van Leemput K, Maes F, Vandermeulen D, et al. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 2001; 20(8): 677-688. – reference: 9)Yamamoto D, Arimura H, Kakeda S, et al. Development of automated method for detection of multiple sclerosis candidate regions based on brain magnetic resonance images, 電子情報通信学会技術研究報告．MI，医用画像2007; 107(220): 51-52. – reference: 11)Bradley PS, Fayyad UM. Refining initial points for k-means clustering. Proceedings of the 15th International Conference on Machine Learning. 1998; 91-99. – reference: 12)Dougherty RP, Kunzelmann K-H. Computing Local Thickness of 3D Structures with ImageJ. Microscopy & Microanalysis 2007 Meeting 2007. – reference: 6)Anbeek P, Vincken KL, van Osch MJ, et al. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 2004; 8(3): 205-215. – reference: 3)Guttmann CR, Kikinis R, Anderson MC, et al. Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility. J Magn Reson Imaging 1999; 9(4): 509-518. |
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| SubjectTerms | computer-aided diagnosis (CAD) k-mean clustering technique magnetic resonance imaging (MRI) multiple sclerosis |
| Title | 頭部MR画像における多発性硬化症のコンピュータ支援診断システムの開発 |
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