Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Robust Real-Time Face Detection
International Journal of Computer Vision
Vector Boosting for Rotation Invariant Multi-View Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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3D knee magnetic resonance (MR) scout scan is an emerging imaging sequence that facilitates technicians in aligning the imaging planes of diagnostic high resolution MR scans. In this paper, we propose a method to automate this process with the goal of improving the accuracy, robustness and speed of the workflow. To tackle the various challenges coming from MR knee scout scans, our auto-alignment method is built upon a redundant, adaptive and hierarchical anatomy detection system. More specifically, we learn 1) a hierarchical redudant set of anatomy detectors, and 2) ensemble of group-wise spatial configurations across different anatomies, from training data. These learned statistics are integrated into a comprehensive objective function optimized using an expectation-maximization (EM) framework. The optimization provides a new framework for hierarchical detection and adaptive selection of anatomy primitives to derive optimal alignment. Being extensively validated on 744 clinical datasets, our method achieves high accuracy (sub-voxel alignment error), robustness (to severe diseases or imaging artifacts) and fast speed (∼5 secs for 10 alignments).