Performance Evaluation of Age Estimation from T1-Weighted Images Using Brain Local Features and CNN

• Published in: Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2018; pp. 694 - 697
• Main Authors: Ito, Koichi, Fujimoto, Ryuichi, Tzu-Wei Huang, Hwann-Tzong Chen, Kai Wu, Sato, Kazunori, Taki, Yasuyuki, Fukuda, Hiroshi, Aoki, Takafumi
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2018
• Subjects: Aging; Biomedical imaging; Estimation; Feature extraction; Image databases; Primary motor cortex
• ISSN: 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2018.8512443

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.