Age estimation using effective brain local features from T1-weighted images

Ryuichi Fujimoto; Chihiro Kondo; Koichi Ito; Kai Wu; Kazunori Sato; Yasuyuki Taki; Hiroshi Fukuda; Takafumi Aoki

doi:10.1109/EMBC.2016.7592081

Age estimation using effective brain local features from T1-weighted images

Ryuichi Fujimoto, Chihiro Kondo, Koichi Ito, Kai Wu, Kazunori Sato, Yasuyuki Taki, Hiroshi Fukuda, Takafumi Aoki

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

This paper proposes a simple method of selecting effective brain local features for age estimation from T1-weighted MR images. We also employ the high-resolution AAL atlas, which is defined by 1,024 local regions, to improve the accuracy of age estimation. We evaluate performance of the proposed method using 1,099 T1-weighted images from a large-scale brain MR image database of healthy Japanese, and demonstrate that the proposed method exhibits efficient performance of age estimation compared with conventional methods.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5941-5944
Number of pages	4
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7592081
Publication status	Published - 2016 Oct 13
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 2016 Aug 16 → 2016 Aug 20

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2016-October
ISSN (Print)	1557-170X

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	16/8/16 → 16/8/20

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10.1109/EMBC.2016.7592081

Cite this

Fujimoto, R., Kondo, C., Ito, K., Wu, K., Sato, K., Taki, Y., Fukuda, H., & Aoki, T. (2016). Age estimation using effective brain local features from T1-weighted images. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 5941-5944). Article 7592081 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7592081

Fujimoto, Ryuichi ; Kondo, Chihiro ; Ito, Koichi et al. / Age estimation using effective brain local features from T1-weighted images. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 5941-5944 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Age estimation using effective brain local features from T1-weighted images",

abstract = "This paper proposes a simple method of selecting effective brain local features for age estimation from T1-weighted MR images. We also employ the high-resolution AAL atlas, which is defined by 1,024 local regions, to improve the accuracy of age estimation. We evaluate performance of the proposed method using 1,099 T1-weighted images from a large-scale brain MR image database of healthy Japanese, and demonstrate that the proposed method exhibits efficient performance of age estimation compared with conventional methods.",

author = "Ryuichi Fujimoto and Chihiro Kondo and Koichi Ito and Kai Wu and Kazunori Sato and Yasuyuki Taki and Hiroshi Fukuda and Takafumi Aoki",

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doi = "10.1109/EMBC.2016.7592081",

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Fujimoto, R, Kondo, C, Ito, K, Wu, K, Sato, K, Taki, Y, Fukuda, H & Aoki, T 2016, Age estimation using effective brain local features from T1-weighted images. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016., 7592081, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, Institute of Electrical and Electronics Engineers Inc., pp. 5941-5944, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/8/16. https://doi.org/10.1109/EMBC.2016.7592081

Age estimation using effective brain local features from T1-weighted images. / Fujimoto, Ryuichi; Kondo, Chihiro; Ito, Koichi et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 5941-5944 7592081 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Fujimoto, Ryuichi

AU - Kondo, Chihiro

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AU - Wu, Kai

AU - Sato, Kazunori

AU - Taki, Yasuyuki

AU - Fukuda, Hiroshi

AU - Aoki, Takafumi

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N2 - This paper proposes a simple method of selecting effective brain local features for age estimation from T1-weighted MR images. We also employ the high-resolution AAL atlas, which is defined by 1,024 local regions, to improve the accuracy of age estimation. We evaluate performance of the proposed method using 1,099 T1-weighted images from a large-scale brain MR image database of healthy Japanese, and demonstrate that the proposed method exhibits efficient performance of age estimation compared with conventional methods.

AB - This paper proposes a simple method of selecting effective brain local features for age estimation from T1-weighted MR images. We also employ the high-resolution AAL atlas, which is defined by 1,024 local regions, to improve the accuracy of age estimation. We evaluate performance of the proposed method using 1,099 T1-weighted images from a large-scale brain MR image database of healthy Japanese, and demonstrate that the proposed method exhibits efficient performance of age estimation compared with conventional methods.

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Fujimoto R, Kondo C, Ito K, Wu K, Sato K, Taki Y et al. Age estimation using effective brain local features from T1-weighted images. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 5941-5944. 7592081. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7592081

Age estimation using effective brain local features from T1-weighted images

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