Table 1.
Demographic information of the subjects involved in the study.
Figure 1.
Overview of our proposed method.
Figure 2.
Illustration of hierarchical ROIs.
Left: Hierarchical ROIs in three different layers; Right: Network connections between ROIs within different layers.
Table 2.
Number of ROIs in the hierarchy.
Figure 3.
Explanation of the network model.
Left: Two types of nodes are included in the hierarchical network: the simple node in , and the compound node in
(
). Each compound node is obtained by grouping several simple nodes agglomeratively. Right: Two types of edges are included in the hierarchical network, each modeling the within-layer and between-layer interactions, respectively.
Figure 4.
Explanation of the membership matrix.
The -th row in the membership matrix
represents the composition of the node
in
. In our example, since
is composed of the simple nodes
,
and
in
, the elements of
,
and
in
are set to
, while the others in the
-th row are set to
.
Figure 5.
Overview of the proposed classification scheme.
Figure 6.
Classification comparison using different features.
The classification performance is compared between our proposed method (four-layer network features as in Method I) and the conventional volumetric method (Method IV) on 20 training/test groups. Each group contains 150 training samples and 75 test samples randomly partitioned from our data set.
Figure 7.
Classification comparison using different hierarchical structure.
The classification performance is compared between the four-layer network features in Method I and the single layer network features in Method II on 20 training/test groups. Each group contains 150 training samples and 75 test samples randomly partitioned from our data set.
Table 3.
Comparison of discrimination efficacy of different features.
Figure 8.
Classification comparison using network features and volumetric features with different numbers of training samples.
Table 4.
Configurations of classification Schemes.
Figure 9.
Comparison of seven classification schemes on network features.
The classification accuracy is plotted over different number of training samples. For a given number of training samples, the classification accuracy is averaged over 20 training/test groups randomly partitioned from our data set using this number of training samples. The scheme configurations are shown in Table 4.
Table 5.
Selected discriminative features.
Figure 10.
Comparison of different metrics used for modeling the regional interactions.
The classification accuracy is plotted over different number of training samples. For a given number of training samples, the classification accuracy is averaged over 20 training/test groups randomly partitioned from our data set using this number of training samples.
Table 6.
Comparison of different metrics for modeling the regional interactions.