DocumentCode :
3520149
Title :
Target-oriented shape modeling with structure constraint for image segmentation
Author :
Zhang, Wuxia ; Yuan, Yuan ; Li, Xuelong ; Yan, Pingkun
Author_Institution :
State Key Lab. of Transient Opt. & Photonics, Xi´´an Inst. of Opt. & Precision Mech., Xi´´an, China
fYear :
2011
fDate :
28-28 Nov. 2011
Firstpage :
194
Lastpage :
198
Abstract :
Image segmentation plays a critical role in medical imaging applications, whereas it is still a challenging problem due to the complex shapes and complicated texture of structures in medical images. Model based methods have been widely used for medical image segmentation as a priori knowledge can be incorporated. Accurate shape prior estimation is one of the major factors affecting the accuracy of model based segmentation methods. This paper proposes a novel statistical shape modeling method, which aims to estimate target-oriented shape prior by applying the constraint from the intrinsic structure of the training shape set. The proposed shape modeling method is incorporated into a deformable model based framework for image segmentation. The experimental results showed that the proposed method can achieve more accurate segmentation compared with other existing methods.
Keywords :
image segmentation; medical image processing; statistical analysis; complex shapes; complicated structure texture; deformable model based framework; medical image segmentation; medical imaging applications; shape prior estimation; statistical shape modeling method; structure constraint; target-oriented shape estimation; target-oriented shape modeling; training shape set; Image segmentation; Shape measurement; Image Segmentation; Manifold Assumption; Manifold Learning; Shape Modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ACPR), 2011 First Asian Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-0122-1
Type :
conf
DOI :
10.1109/ACPR.2011.6166707
Filename :
6166707
Link To Document :
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