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3.3 Global Deformation using CSTPF
It has been proved that CSFPF preformed well in
local deformation in last paragraph, now let’s
discuss how it perform in global deformation.
Figure.3 is a contrast of the global deformations
using the elastic registration approach base on TPS
、CSTPF、CSRBF with manual landmarks. In this
Figure shows we can see that the deformations using
the elastic registration approach base on CSTPF
(Fig.3 (b)) and TPS (Fig.3 (c)) are almost the same.
Figure.3(e) takes one line out of the deformation’s
results and makes a comparison. It is shown that the
deformation’s lines of CSTPF and TPS are almost
superposed. This result illustrated that image
deformation using CSTPF can keep the advantage of
TPS in global deformation, which can not be
achieved by using CSRBF.
(a) Landmarks (b) TPS (c) CSTPF
(d) CSRBF (e)
Figure 3: Global deformation contrast (support radius
1000
=r
) : (a) Landmarks’ position; (b) global
deformations using TPS; (c) global deformations using
CSTPF; (d) global deformations using CSRBF; Contrast
of the third line of Figure 3 (b)(c)(d), Notice Figure 3 (b)
and (c) are almost superposed.
Experimental results have proved that image
deformation using the elastic registration approach
base on CSTPF is better than CSRBF.
To better illuminate the problem and aiming to
compare the bending energy cost at different support
radii, we experimented on 6 groups of deformations
with random landmarks using the elastic registration
approach based on CSTPF and CSRBF. Figure.4
shows the deformations’ bending energy cost at
different radii. In this graph, it is evident that image
deformation using CSTPF costs less bending energy
than CSRBF.
Consequently, the analysis and experiments in
this chapter indicate that image global deformation
using the elastic registration approach bases on
CSTPF is similar to those on TPS. Furthermore, it is
capable to localize the image deformation domain
while TPS can not. In local image deformation,
utilization of the elastic registration approach bases
on CSTPF costs less bending energy than CSRBF
with the same support radius.
(a) Deformations with 6 random landmarks
(b) Deformations with 10 random landmarks
Figure 4: Contrast of deformations’ bending energy cost
with random landmarks usizng CSTPF and CSRBF (X-
axis: support radius, Y-axis is deformation’s bending
energy cost. real line in figure: CSRBF’s energy cost,
broken line in figure: CSTPF’s energy cost): (a)
Deformations with 6 random landmarks; (b) Deformations
with 10 random landmarks.
4 EXPERIMENTAL RESULTS OF
MEDICAL IMAGES
In this chapter, we have prepared two experiments in
practical situations. With manual landmark, we
compared the registration results for medical images
using the elastic registration approach base on
CSTPF and CSRBF.
In Figure 5, we can compare the results of
deformation using CSTPF and CSRBF. They look
similar but definitely not the same. Observing their
edge comparison (Figure 5 (d) and (e)), it is revealed
that after deformation, figure 5(d) has more edge
information than figure 5(e) (as shown by the
arrowhead), which means more information was
saved by using CSTPF than CSRBF.
Finally, we employed another experiment to
demonstrate that global deformation using CSTPF is
better than CSRBF. In this experiment, we used an
image of deferent mode, figure.6 (a) is MRI image
and figure 6 (b) is CT image. It can be easily noticed
that the source image and target image are just the
same as they have no deformation. However,
because we get landmarks manually, it is liable to
have some artificial errors which are, however,
considered as allowable errors. Given that these
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