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current voxel x is located inside the contour C(x) within the local image X x .
According to Eq. 5.5 , when a voxel, located at position y, is classified as belonging
to the object, i.e. R(x) = 1, then a point x in its vicinity has a high probability of
being marked as the object (i.e. L(y) ? 1) and is less likely to be considered as the
background (L(y) ? -1).
Maximising the posterior distribution in Eq. 5.4 is equivalent to minimising its
negative logarithm, and thus the energy function can be presented as:
E ¼ Z H 0 ð u ð x ÞÞ X
f Z
2
½log P ð I ð y Þj y 2 X i \ X x Þ
X i \ X x
i¼1
ð 5 : 7 Þ
þ log P ð L ð y Þj y 2 X i \ X x Þ dy M i ð u ð y ÞÞg dx
þ l Z jr H ð u ð x ÞÞj dx
where M 1 (/(y)) = H(/) and M 2 (/(y)) = 1-H(/), and H 0 ( ) denotes the derivative
of Heaviside function:
(
H ð / Þ ¼ 0 ;
if / 0
1 ;
if / [ 0
and the associated Euler-Lagrange equation can be then obtained as:
þ log p 1
p 2
þ v Z L ð x Þ k r ð x ; y Þ dy
r /
jr / j
o /
ot ¼ d ð / Þ l div
p 1 ¼ Z
X 1 \ X x
exp ð ð l 1 ð x Þ I ð y ÞÞ 2
2r 1 ð x Þ
1
2p
p
dy
r 1
!
ð 5 : 8 Þ
Z
exp ð l 2 ð x Þ I ð y ÞÞ 2
2r 2 ð x Þ
1
2p
p 2 ¼
p
dy
r 2
X 2 \ X x
where p 1 and p 2 denote the probability density distribution of the object and
background, respectively.
5.3.2 Slice-by-Slice Correction
The segmentation results obtained from the first stage are rather accurate. How-
ever, due to the complexity of medical images and associated artefacts, these may
contain outliers, such as kissing non-arterial vessels and other fragments (e.g. as
shown in Fig. 5.5 a). In order to further improve segmentation performance, we
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