Biomedical Engineering Reference
In-Depth Information
Table 7.1: Statistics for preprocessing: number of meta-cells (no. of MC),
times for meta-cell generation (MC generation), gradient computation
(gradient), interval tree construction (IT), size of each meta-cell (MC size), and
size of the interval tree (IT size)
Data set
Artery
Artery2
Kidney
ColorV
Size (MB)
3
.
37
20
.
97
4
.
57
63
.
08
No. of MC
125
1000
7600
125
MC generation (sec)
3
25
5
58
Gradient (sec)
16
88
24
1740
IT (sec)
0
.
5
0
.
5
0
.
5
1
.
5
Total (sec)
20
114
30
1801
MC size (kB)
343
04
285
696
8
2944
2718
72
.
.
.
.
IT size (kB)
38
62
379
13
938
95
176
01
.
.
.
.
As we already said, T-surfaces uses auxiliary and very memory consuming
data structures. We certainly can design optimizations. However, by now, we
had to use a machine with enough memory to manage the T-surfaces structures.
The machine used is Pentium III, 863 MHz with 512 MB of RAM and 768 MB of
swap space.
There are three main steps to be considered: preprocessing, isosurfaces gen-
eration, and T-surfaces evolution. Preprocessing encompasses the gradient com-
putation and meta-cell generation. Meta-cell generation is basically divided into
two steps: (a) mapping data points into meta-cells and writing data information
to the corresponding meta-cells; and ( b) finding meta-intervals and computing
the interval tree. As can be seen in Table 7.1, preprocessing step can be expen-
sive due to the gradient computation. Also, we observe from this table that the
interval tree size (last row) is very much smaller than the bound computed in
Section 7.7 (8 MB).
Isosurfaces generation encompasses steps (1) and (2) of the algorithm in
Section 7.7. Table 7.2 reports some performance statistics for this step. In this
case, we use a data cache of 15 MB.
It is important to observe that, in general, the smaller the meta-cell size, the
faster the isosurface search. This fact is verified in Table 7.2 in which we vary
the number of meta-cells used for the kidney data set. For instance, when using
7600 meta-cells, the algorithm can fetch all the sets of active meta-cells from
disk. Thus, there are no extra I/O operations during step (1) of the segmentation
