Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
Embedding Gestalt Laws in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using shape distributions to compare solid models
Proceedings of the seventh ACM symposium on Solid modeling and applications
ACM Transactions on Graphics (TOG)
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Boundary Finding with Prior Shape and Smoothness Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
The Use of Force Histograms for Affine-Invariant Relative Position Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Markov Dependence Tree-Based Segmentation of Deep Brain Structures
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Modeling interaction for segmentation of neighboring structures
IEEE Transactions on Information Technology in Biomedicine
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Hi-index | 0.00 |
In this paper we develop a multi-object prior shape model for use in curve evolution-based image segmentation. Our prior shape model is constructed from a family of shape distributions (cumulative distribution functions) of features related to the shape. Shape distribution-based object representations possess several desired properties, such as robustness, invariance, and good discriminative and generalizing properties. Further, our prior can capture information about the interaction between multiple objects. We incorporate this prior in a curve evolution formulation for shape estimation. We apply this methodology to problems in medical image segmentation.