Biomedical Engineering Reference
In-Depth Information
Since the three components of the surface are independently defined, to
minimize E 2 , we minimize E x , E y , and E z , separately. To minimize
N
E x =
[ x ( u j ,v j ) X j ] 2
(7.8)
,
j = 1
since
n
x ( u j ,v j ) =
x i g i ( u j ,v j ) ,
(7.9)
i = 1
we minimize
x i g i ( u j ,v j ) X j 2
N
n
E x =
(7.10)
.
j = 1
i = 1
This involves determining the partial derivatives of E x with respect to the x i 's,
setting the partial derivatives to zero and solving the obtained system of equa-
tions. This results in
N
n
g k ( u j ,v j )
[ x i g i ( u j ,v j ) X j ] = 0; k = 1 ,..., n .
(7.11)
j = 1
i = 1
This represents a system of n linear equations, which can be solved for { x i :
i = 1 ,..., n } . Since RaG basis functions monotonically decrease from a center
point, if σ is not very large, Eq. (7.11) will have a diagonally dominant matrix
of coefficients, ensuring a solution. In the same manner, { y i : i = 1 ,..., n } and
{ z i : i = 1 ,..., n } can be determined by minimizing E y and E z , respectively. Note
that the above process positions the n control points of a RaG surface so that
the surface will approximate the N image voxels with the least sum of squared
errors. n depends on the size and complexity of the shape being approximated.
n is typically a few hundred.
Since shape voxels are mapped to a sphere, spherical parameters are ob-
tained for them. Assuming the approximating surface is represented by P ( u ,v ),
the distance of voxel V i = ( x i , y i , z i ) to the surface is estimated from E ( u i ,v i ) =
|| V i P ( u i ,v i ) || . The adjacency information between the control points is pro-
vided in the u and v parameter coordinates. Therefore, index i is arbitrary and
the control points with their associated nodes can be rearranged in Eq. (7.1)
without having any effect in the obtained surface.
When the standard deviation in a RaG surface is very small, the surface fol-
lows individual voxels. The selected standard deviation should be large enough
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