TITLE:
Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging
AUTHORS:
Yasuo Yamashita, Hidetaka Arimura, Takashi Yoshiura, Chiaki Tokunaga, Ohara Tomoyuki, Koji Kobayashi, Yasuhiko Nakamura, Nobuyoshi Ohya, Hiroshi Honda, Fukai Toyofuku
KEYWORDS:
Computer-aided Classification (CAD); Alzheimer’s Disease; Magnetic Resonance Imaging (MRI); Arterial Spin Labeling (ASL); Fuzzy Membership Image; Cortical Thickness; Cerebral Blood Flow (CBF)
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.6 No.11,
November
25,
2013
ABSTRACT:
Alzheimer’s disease (AD) is a dementing disorder and one
of the major public health problems in countries with greater longevity. The
cerebral cortical thickness and cerebral blood flow (CBF), which are considered
as morphological and functional image features, respectively, could be
decreased in specific cerebral regions of patients with dementia of Alzheimer
type. Therefore, the aim of this study was to develop a computer-aided
classification system for AD patients based on machine learning with the
morphological and functional image features derived from a magnetic resonance
(MR) imaging system. The cortical thicknesses in ten cerebral regions were
derived as morphological features by using gradient vector trajectories in
fuzzy membership images. Functional CBF maps were measured with an arterial
spin labeling technique, and ten regional CBF values were obtained by
registration between the CBF map and Talairach atlas using an affine
transformation and a free form deformation. We applied two systems based on an
arterial neural network (ANN) and a support vector machine (SVM), which were
trained with 4 morphological and 6 functional image features, to 15 AD patients
and 15 clinically normal (CN) subjects for classification of AD. The area under
the receiver operating characteristic curve (AUC) values for the two systems
based on the ANN and SVM with both imagefeatures were 0.901 and 0.915, respectively. The
AUC values for the ANN-and SVM-based systems with the morphological features
were 0.710 and 0.660, respectively, and those with the functional features were
0.878 and 0.903, respectively. Our preliminary results suggest that the
proposed method may have potential for assisting radiologists in the
differential diagnosis of AD patients by using morphological and functional
image features.