3D knowledge-based segmentation using pose-invariant higher-order graphs

Authors:
Chaohui Wang;Olivier Teboul;Fabrice Michel;Salma Essafi;Nikos Paragios
Affiliations:
Laboratoire MAS, Ecole Centrale de Paris, France and Equipe GALEN, INRIA Saclay - Île de France, Orsay, France;Laboratoire MAS, Ecole Centrale de Paris, France and Microsoft France;Laboratoire MAS, Ecole Centrale de Paris, France and Equipe GALEN, INRIA Saclay - Île de France, Orsay, France;Laboratoire MAS, Ecole Centrale de Paris, France and Equipe GALEN, INRIA Saclay - Île de France, Orsay, France;Laboratoire MAS, Ecole Centrale de Paris, France and Equipe GALEN, INRIA Saclay - Île de France, Orsay, France
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

Citing 7
Cited 4

Boundary Finding with Parametrically Deformable Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application

Computer Vision and Image Understanding
Graphical models for machine learning and digital communication

Graphical models for machine learning and digital communication
Random Forests

Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Wavelet-driven knowledge-based MRI calf muscle segmentation

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Fast IIR Isotropic 2-D Complex Gabor Filters With Boundary Initialization

IEEE Transactions on Image Processing

Pose-invariant 3d proximal femur estimation through bi-planar image segmentation with hierarchical higher-order graph-based priors

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Probabilistic multi-shape segmentation of knee extensor and flexor muscles

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Prior knowledge, random walks and human skeletal muscle segmentation

MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

Segmentation is a fundamental problem in medical image analysis. The use of prior knowledge is often considered to address the ill-posedness of the process. Such a process consists in bringing all training examples in the same reference pose, and then building statistics. During inference, pose parameters are usually estimated first, and then one seeks a compromise between data-attraction and model-fitness with the prior model. In this paper, we propose a novel higher-order Markov Random Field (MRF) model to encode pose-invariant priors and perform 3D segmentation of challenging data. The approach encodes data support in the singleton terms that are obtained using machine learning, and prior constraints in the higher-order terms. A dual-decomposition-based inference method is used to recover the optimal solution. Promising results on challenging data involving segmentation of tissue classes of the human skeletal muscle demonstrate the potentials of the method.