Target-oriented shape modeling with structure constraint for image segmentation
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.