Constrained Marginal Space Learning for Efficient 3D Anatomical Structure Detection in Medical Images

Recently, we proposed marginal space learning (MSL) as a generic approach for automatic detection of 3D anatom- ical structures in many medical imaging modalities. To accurately localize a 3D object, we need to estimate nine parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of uniformly search- ing the original nine-dimensional parameter space, only low-dimensional marginal spaces are uniformly searched in MSL, which significantly improves the speed. In many real applications, a strong correlation may exist among pa- rameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. In this paper, we propose constrained MSL to exploit this correlation for further speed-up. As another major contri- bution, we propose to use quaternions for 3D orientation representation and distance measurement to overcome the inherent drawbacks of Euler angles in the original MSL. The pro...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Yefeng Zheng, Bogdan Georgescu, Haibin Ling, Shaohua Kevin Zhou, Michael Scheuering, Dorin Comaniciu
• Keywords: Anatomical Structures, Computer Vision, CVPR 2009, Object Detection