Paper Info
Title
Performance Evaluation of Age Estimation from T1-Weighted Images Using Brain Local Features and CNN.
Abstract
The age of a subject can be estimated from the brain MR image by evaluating morphological changes in healthy aging. We consider using two-types of local features to estimate the age from T1-weighted images: handcrafted and automatically extracted features in this paper. The handcrafted brain local features are defined by volumes of brain tissues parcellated into 90 or 1,024 local regions defined by the automated anatomical labeling atlas. The automatically extracted features are obtained by using the convolutional neural network (CNN). This paper explores the difference between the handcrafted features and the automatically extracted features. Through a set of experiments using 1,099 T1-weighted images from a Japanese MR image database, we demonstrate the effectiveness of the proposed methods, analyze the effectiveness of each local region for age estimation and discuss its medical implication.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512443
EMBC
Field
DocType
Volume
Computer vision,Convolutional neural network,Medical imaging,Computer science,Feature extraction,Artificial intelligence,Image database
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
9
Name
Order
Citations
PageRank
Koichi Ito1185.48
Ryuichi Fujimoto22414.44
Tzu-Wei Huang351.64
Hwann-Tzong Chen482652.13
Kai Wu501.01
Kazunori Sato6143.78
Yasuyuki Taki712514.22
Hiroshi Fukuda8102.03
Takafumi Aoki9915125.99