Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images

Recently, marginal space learning (MSL) was proposed as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities [1]. To accurately localize a 3D object, we need to estimate nine pose parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of exhaustively searching the original nine-dimensional pose parameter space, only low-dimensional marginal spaces are searched in MSL to improve the detection speed. In this paper, we apply MSL to 2D object detection and perform a thorough comparison between MSL and the alternative full space learning (FSL) approach. Experiments on left ventricle detection in 2D MRI images show MSL outperforms FSL in both speed and accuracy. In addition, we propose two novel techniques, constrained MSL and nonrigid MSL, to further improve the efficiency and accuracy. In many real applications, a strong correlation may exist among pose parameters in the same marginal spaces. F...

• Type: Conference
• Year: 2009
• Where: IPMI
• Authors: Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu
• Keywords: IPMI 2009, Medical Imaging, Nonrigid Msl, Object Detection, Original Msl