Biomedical Engineering Reference
In-Depth Information
Figure 11.23:
Echocardiographic image with high level of noise and gaps.
In the top right we see a graph of the final segmentation function. In the middle
row we see its histogram (left) and zoom of the histogram around max(
u
) (right).
By that we take level 0.057 for visualization of the boundary of segmented object
(top left). In the bottom row we present the result of segmentation using 5
×
5
convolution mask. Such a result is a bit smoother and 59 time steps (CPU
time
=
5
.
65 sec) were used.
For visualization of the segmentation level line in further figures, we use the
same strategy as above, i.e. the value of
u
just below the last peak of histogram
(corresponding to upper “flat region”) is chosen. In segmentation of the right
atrium, presented in Fig. 11.25, we took the same parameters as above and no
presmoothing was applied. CPU time for 79 time steps was 7.59 sec. In segmen-
tation of the left and right ventricles, with more destroyed boundaries, we use
K
=
0
.
5 and we apply 5
×
5 convolution mask (other parameters were same as
above). Moreover, for the left ventricle we use double-peak-like initial function
(see Fig. 11.26 (top)) to speed up the process for such highly irregular object. In
that case 150 time steps (CPU time
=
14.5 sec) were used. For the right ventricle,
67 time steps (CPU time
=
6.57 sec) were necessary to get segmentation result,
see Fig. 11.27.
In the last example given in Fig. 11.28, we present segmentation of the mam-
mography (165
×
307 pixels). Without presmoothing of the given image and with
parameters
ε
=
10
−
1
,
K
=
0
.
1,
τ
=
0
.
0001,
v
=
1, TOL
=
10
−
3
, and
δ
=
10
−
5
we
get the segmentation after 72 time steps. Since there are no big gaps, we take
larger
ε
and since the object is small (found in a shorter time) we use smaller
time step
τ
.
