@inproceedings{b027960e3e044cbaae6d230789437ae7,
title = "Target-oriented shape modeling with structure constraint for image segmentation",
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, Manifold Assumption, Manifold Learning, Shape Modeling",
author = "Wuxia Zhang and Yuan Yuan and Xuelong Li and Pingkun Yan",
year = "2011",
doi = "10.1109/ACPR.2011.6166707",
language = "英语",
isbn = "9781457701221",
series = "1st Asian Conference on Pattern Recognition, ACPR 2011",
pages = "194--198",
booktitle = "1st Asian Conference on Pattern Recognition, ACPR 2011",
note = "1st Asian Conference on Pattern Recognition, ACPR 2011 ; Conference date: 28-11-2011 Through 28-11-2011",
}