Brain age estimation from T1-weighted images using effective local features

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Statistical analysis using large-scale brain magnetic resonance (MR) image databases has examined that brain tissues have age-related morphological changes. The age of a subject can be estimated from the brain MR image by evaluating morphological changes with healthy aging. This paper proposes an age estimation method using local features of T1-weighted MR images. The brain local features are defined by volumes of brain tissues parcellated into 1,024 local regions defined by the automated anatomical labeling atlas. This paper also proposes the effective local feature selection method to improve the accuracy of age estimation. We evaluate the accuracy of the proposed method using 1,099 T1-weighted images from a Japanese MR image database. We also analyze effectiveness of each local region for age estimation and discuss its medical implication.

Original languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3028-3031
Number of pages4
ISBN (Electronic)9781509028092
DOIs
Publication statusPublished - 2017 Sep 13
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: 2017 Jul 112017 Jul 15

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
CountryKorea, Republic of
CityJeju Island
Period17/7/1117/7/15

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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