G. X. LI ET AL.

Copyright © 2011 SciRes. OJMI

47

Taitnes on liveing sam

C RPre-t PostImpent

ble 3. Fs resultsr trainnples.

aseotation Alignmen-Alignment rovem

1 0.0385 0.3565 0.3543 0.6%

2 0.0405 0.6078 0.6012 1.1%

3 –0.0012 0.4422 0.4467 –1%

Buss me distance metric is spherical.

te evaluation by plane distance w in-

proposed a feature based alignment

set of training shapes for SSM model

olleagues and friends at the

ogy for their research

on medical image processing. Especially thank interna-

, “Statistical Shape Mod-

edical Image Segmentation: A review,”

Analysis, Vol. 13, No. 4, 2009, pp. 543-

t on the Gau

proxima

ap, th

Its ap

d

ould

uce the measurement error. Especially when the oriental

deviate of the training sample is large, as the case 4 in

Table 1 showed. Another sensible factor to the align-

ment accuracy is that the representative points generated

by K-means algorithm could not reflect the distribution

density of the mapping points. A better way to solve this

problem is using classifier method to assign the weight to

each representative point.

5. Conclusions

In this paper, we

method to match a

building. Considering the shape correspondence proc-

essing of the model building, we proposed a feature

based method and transform the 3D object alignment

problem into 2D Gaussian spherical space. Here, we in-

troduced the piecewise Gauss map theoretical foundation

and gave an approximated solution for triangular po-

lygonal surface. As well, we proposed using representa-

tive points to reflect the modality of the point sets on

Gauss map. While the K-means based clustering method

was employed to obtain these representative points. The

experimental results on the left lung and liver training

samples showed the availability of the proposed method.

However, in theory, this method is not elaborate yet,

because using Gauss map cannot describe the character

of surface reliably all the time.

As the future works, we will improve the surface para-

meterization method by conformal geometry theory. Ref-

erence [11] provides a previous works in this area. It is

undoubtedly a convictive way for the model alignment

and landmarks registration. As well, we will use sphere-

cal distance to replace the plane Euclidean distance when

metric the points on Gauss map. At last but not the least,

the classifier algorithm is expected to get a better de-

scription of the distributional regularity of surface points.

6. Acknowledgements

We would like to thank our c

Kyushu Institute of Technolbasis

tional exchange student Eloi Duchaussoy for always

having an open ear for many smart ideas to me. Thank T.

Heimann at Medical and Biological Infomatics at the

German Cancer Research Center. Without his kindly

help about the programming of triangular mesh structure,

we couldn’t complete this research on time. He is very

accommodating and intelligent.

7. References

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