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Algorithm III Optimization of the joint con
guration
1. For each component V i , run the step 1 and 2 of the inner iteration to
compute {X i ; k ¼
0
;
1
; ...; K
1
g
with their correspondent beliefs
1}.
2. Run the outer iteration to compute the joint distribution p X
{ x i ;
k
¼
0
;
1
; ...;
K
ðÞ
j
I
and update
fx i g:
3. For each component V i , run the step 3 of the inner iteration to update the
con
marginal distribution
guration of each component.
4. Repeat 1
-
3 until the procedure converges.
filter based inner iteration and the BP based outer iteration are
combined such that
The particle
￿
The particle
filtering in the inner iteration is used to
find probable candidates for
each individual component.
￿
The belief propagation (BP) in the outer iteration is used to set regularization on
multiple components so that only the candidates that can ful
ll the inter-com-
ponent constraint will be selected.
In our algorithm, the stop criteria is set to run Algorithm III for a
xed number
guration of Vi i is set as the sample X i with the
of iterations and the optimal con
highest probability x i .
2.5 The Determination of the Number of Vertebrae
Vertebral number determination is a key factor for correct vertebral body detection.
In our approach we handle this problem by a semi-automatic method as described
below:
User clicks two landmarks on the X-ray image to indicate the centers of the
rst
￿
and the last visible vertebral bodies.
From the locations of these two landmarks and the projection parameters of the
X-ray image(s), we can estimate a minimal number of vertebrae N upper ,by
dividing the distance between the two landmarks by an empirical estimation of
the average vertebral height. Usually N upper is greater than the true number N of
the visible vertebrae in the image. We then construct a graph model with N upper
nodes.
￿
Carry out the inference procedure described in Algorithm III. Due to the dis-
tance potential between neighboring components as shown in Fig. 3 , neigh-
boring components will either be located side-by-side or overlap with each
￿
 
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