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

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128 Chapter 5 Quantitative shape analysis of hippocampus in AD<br />

Specificity<br />

Specificity<br />

0.805 0.815 0.825 0.835<br />

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1e−04 1e−03 1e−02 1e−01 1e+00<br />

Threshold α<br />

(a) OOB specificity, LR, MR<br />

0.74 0.78 0.82 0.86<br />

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1e−04 1e−03 1e−02 1e−01 1e+00<br />

Threshold α<br />

(c) OOB specificity, LS, MR<br />

Specificity<br />

Specificity<br />

0.70 0.72 0.74 0.76 0.78<br />

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1e−04 1e−03 1e−02 1e−01 1e+00<br />

Threshold α<br />

(b) OOB specificity, LR, MS<br />

0.72 0.76 0.80 0.84<br />

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Threshold α<br />

(d) OOB specificity, LS, MS<br />

Figure 5.9: The out-of-bag (OOB) estimates of specificity of the disease classification<br />

using bagged support vector machines (SVMs) with varying thresholds<br />

in the l<strong>and</strong>mark selection. Black circles: shape features from the Statistical<br />

<strong>Shape</strong> Mo<strong>de</strong>l (SSM) only; red circles: shape features with additional volume<br />

features.<br />

of education, in or<strong>de</strong>r to isolate the disease from these effects.<br />

5.6.3 Disease classification using SSM<br />

Using the SSM <strong>de</strong>scriptors combining both size <strong>and</strong> shape information provi<strong>de</strong>d<br />

better discrimination than using only hippocampal volume to classify AD from<br />

NC. In general the <strong>de</strong>scriptors of size-<strong>and</strong>-shape from MR outperformed features<br />

produced by MS, because volume alone is a good discriminant. Since the changes<br />

mo<strong>de</strong>led by MS were driven by both size <strong>and</strong> shape, adding volume to the features<br />

extracted using MR <strong>and</strong> MS increased both of their accuracy, but to a much<br />

less extent for MR compared to MS. Using shape information therefore provi<strong>de</strong>s<br />

additional discrimination power to volumetry.

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