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

68 Chapter 3 Hippocampal segmentation using multiple atlases
DSC
0.82 0.83 0.84 0.85 0.86 0.87
20 40 60 80 100 120
No. of selected atlases
λ=0.1
λ=0.3
λ=0.5
λ=0.7
λ=0.9
λ=1
Figure 3.7: The average Dice similarity coefficient (DSC) of left and right hippocampi
using locally weighted voting (LWV) on the normal control (NC) atlas
set. The atlases selected by maximal marginal relevance (MMR) re-ranking.
The parameter λ varies from 0.1 to 0.9. The case of λ = 1 is equivalent to the
selection by NMI ranking.
atlases selected according to the image similarity ranking. Combining the atlases
selected by MMR reaches accuracy that required more atlases based on similarity
selection. MMR is therefore a more efficient strategy when the number of atlases is
restricted by the limitations of computation time, memory usage or the availability
of atlases.
The effect of using MMR is less significant on the AD atlas set. It may be due
to more variability within the AD atlases introducing more noise into the penalty
term of MMR. The LAR criterion achieved better performance when there were
20–40 atlases were selected and fused on the AD atlases.
As a parametric method, MMR is dependent upon the parameter weighting the
inter-atlas similarity. In terms of computation complexity, the LAR algorithm
is more efficient since it does not require the step to pre-compute the inter-atlas
similarity matrix as in MMR based method.