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Chapter 3 Hippocampal segmentation using multiple atlases 67
We varied the power p of the MSD in the atlas weight (eq. 3.14) in order to choose
the optimal value of p to be used in the LWV. The results of LWV on the NC
atlas set with varying values of the power in the atlas weight is plotted in Figure
3.6, which shows that p = −3 gave the best performance.
DSC
0.80 0.82 0.84 0.86 0.88
20 40 60 80 100 120
No. of selected atlases
p=−1
p=−2
p=−3
p=−4
p=−5
p=−6
Figure 3.6: The average Dice similarity coefficient (DSC) of left and right
hippocampi using locally weighted voting (LWV) on the normal control (NC)
atlas set with varying power p of the MSD function in the atlas weight. The
atlases are selected by normalized mutual information (NMI) ranking.
In the atlas selection by MMR re-ranking, the parameter λ (eq. 3.24) was adjusted.
The effects of varying values of λ were plotted in Figure 3.7. The best performance
is produced with parameter λ = 0.7.
The leave-one-out cross validation was performed on the NC atlas set and the
AD atlas set. We compared the performance of the image similarity based atlas
selection, MMR re-ranking and LAR atlas selection. The results of average DSC
of left and right hippocampi with increasing number of atlases selected according
to different criteria are shown in Figure 3.8.
3.4.2.3 Discussion
The results show that the MMR re-ranked atlases with λ = 0.7 outperforms other
methods on the NC atlas set. One example of fusing 31 atlases is shown in Fig-
ure 3.5. It selects more informative atlases as compared to the same number of