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

Chapter 5 Quantitative shape analysis of hippocampus in AD 129
Sensitivity
0.84 0.86 0.88 0.90
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1e−04 1e−03 1e−02 1e−01 1e+00
Threshold α
(a) Testing set specificity, LR, MR
Sensitivity
0.80 0.85 0.90
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1e−04 1e−03 1e−02 1e−01 1e+00
Threshold α
(c) Testing set specificity, LS, MR
Sensitivity
0.65 0.70 0.75 0.80
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1e−04 1e−03 1e−02 1e−01 1e+00
Threshold α
(b) Testing set specificity, LR, MS
Sensitivity
0.65 0.75 0.85
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1e−04 1e−03 1e−02 1e−01 1e+00
Threshold α
(d) Testing set specificity, LS, MS
Figure 5.10: The specificity of the disease classification using bagged support
vector machines (SVMs) with varying thresholds in the landmark selection on
a separated testing set. Black circles: shape features from the Statistical Shape
Model (SSM) only; red circles: shape features with additional volume features.
Restricting the analysis to the subregions affected by the disease increased the
discrimination ability of the SSM approach by capturing localized differences be-
tween the subpopulations. Whole surface SSMs are able to describe the global
shape or size-and-shape of the biological object, but are not sensitive to the de-
formations limited to specific areas on the object surface. Localizing the PCA to
subregions with significant shape difference (LS) on the surface produced overall
better discrimination between NC and AD than using all the points. When TIV
normalized volume was added as additional features to the shape features, the
best classification results were obtained using the SSM built using MR on the
hippocampal subregions selected by LS.
In particular, using LS in the localization step gave more informative surface masks
than LR when describing the atrophy pattern in the disease classification. Subre-
gional masks derived from rigid-body aligned localization model LR tended to be
predominantly representing changes in global scale due to the volume reduction.