Sung-han Lin1, Chih-Chien Tsai1, Yi-Chun Chen2,3, and Jiun-Jie Wang1
1Department of Medical Imaging and Radiological Sciences, Chang-Gung University, TaoYuan, Taiwan, 2Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, TaoYuan, Taiwan, 3College of Medicine, Chang Gung University, TaoYuan, Taiwan
1Department of Medical Imaging and Radiological Sciences, Chang-Gung University, TaoYuan, Taiwan, 2Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, TaoYuan, Taiwan, 3College of Medicine, Chang Gung University, TaoYuan, Taiwan
The current study developed a novel feature
extraction algorithm which based on disease pathological changes and the spatial
information of disease affected pattern and its surrounding regions. Newly
extracted features showed improved diagnostic accuracy, especially for MCI
patients.
Figure 1. Flowchart of feature extraction. For all four DTI
derived indices in each subject, the feature extraction procedure can be
divided into the primary feature from each anatomical region in the brain (panel
A), and the secondary feature set (panel B), which was derived from the disease
affected pattern. The secondary features were calculated from the product of the
value difference to the distance between two primary features. Only features
were involved in the disease affected pattern and passed the neighborhood
selection were selected.
Figure
3. The selected secondary feature set in four DTI derived indices. The selected secondary feature set was showed in mean diffusivity (21 links), fractional
anisotropy (37 links), axial diffusivity (31 links), and radial diffusivity (13
links), respectively. Blue nodes indicate the anatomical regions in the AAL
template. Links between nodes indelicate the secondary features and the thicker
link means the more significance among classes.
