Docteur de l'université Automatic Segmentation and Shape Analysis ...

Chapter 5 Quantitative shape analysis of hippocampus in AD 123
Percentage of selected landmarks, %
Percentage of selected landmarks, %
40 50 60 70 80 90
50 60 70 80 90 100
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Threshold α
(a) Left, LR
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Threshold α
(c) Right, LR
Percentage of selected landmarks, %
Percentage of selected landmarks, %
0 20 40 60 80 100
0 20 40 60 80 100
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Threshold α
(b) Left, LS
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Threshold α
(d) Right, LS
Figure 5.4: The percentage of landmarks selected, with varying thresholds in
the landmark selection on the significance map of Hotelling’s T 2 test.
plotted in 5.9 and 5.10.
As a baseline, using only the TIV normalized volume gave 77.6%(OOB), 83.5%(test-
ing set) accuracy. The best OOB performance on the training set (81.2% accu-
racy) is achieved using rigid-body aligned SSM (MR) on the selected landmarks
(LS, α = 0.01) with additional volume feature. On the separate testing set the
highest accuracy is 88.9% (MR, LS, α = 0.09).
With the features produced by SSMs alone, the best OOB accuracy is 81.5%,
using the localized SSM (MR, LS, α = 0.01), and the accuracy on the testing set
reaches 87.6% (MR, LS, α = 0.1) The result of the linear C-SVM with this set
of SSM features (MR, LS, α = 0.1) gave 71.8% sensitivity and 94.9% specificity,
which is comparable to the result of 69% sensitivity and 84% specificity reported
by Cuingnet et al. (2010).