Active shape models—their training and application
Computer Vision and Image Understanding
Generation of point-based 3D statistical shape models for anatomical objects
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Statistical Shape Analysis: Clustering, Learning, and Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intrinsic Bayesian Active Contours for Extraction of Object Boundaries in Images
International Journal of Computer Vision
Locality preserving constraints for super-resolution with neighbor embedding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Diffusion maps as a framework for shape modeling
Computer Vision and Image Understanding
Shape Analysis of Elastic Curves in Euclidean Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Imaging Sciences
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
Elastic Geodesic Paths in Shape Space of Parameterized Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimized graph-based segmentation for ultrasound images
Neurocomputing
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Organ shape plays an important role in clinical diagnosis, surgical planning and treatment evaluation. Shape modeling is a critical factor affecting the performance of deformable model based segmentation methods for organ shape extraction. In most existing works, shape modeling is completed in the original shape space, with the presence of outliers. In addition, the specificity of the patient was not taken into account. This paper proposes a novel target-oriented shape prior model to deal with these two problems in a unified framework. The proposed method measures the intrinsic similarity between the target shape and the training shapes on an embedded manifold by manifold learning techniques. With this approach, shapes in the training set can be selected according to their intrinsic similarity to the target image. With more accurate shape guidance, an optimized search is performed by a deformable model to minimize an energy functional for image segmentation, which is efficiently achieved by using dynamic programming. Our method has been validated on 2D prostate localization and 3D prostate segmentation in MRI scans. Compared to other existing methods, our proposed method exhibits better performance in both studies.