The Community for Technology Leaders
RSS Icon
Miami, FL, USA
June 20, 2009 to June 25, 2009
ISBN: 978-1-4244-3992-8
pp: 1343-1350
Xiaoguang Lu , Corp. Res., Integrated Data Syst. Dept., Siemens, Princeton, NJ, USA
B. Georgescu , Corp. Res., Integrated Data Syst. Dept., Siemens, Princeton, NJ, USA
D. Comaniciu , Corp. Res., Integrated Data Syst. Dept., Siemens, Princeton, NJ, USA
Magnetic resonance imaging (MRI) is currently the gold standard for left ventricle (LV) quantification. Detection of the LV in an MRI image is a prerequisite for functional measurement. However, due to the large variations in the orientation, size, shape, and image intensity of the LV, automatic LV detection is challenging. In this paper, we propose to use marginal space learning (MSL) to exploit the recent advances in learning discriminative classifiers. Unlike full space learning (FSL) where a monolithic classifier is trained directly in the five dimensional object pose space (two for position, one for rotation, and two for anisotropic scaling), we train three detectors, namely, the position detector, the position-orientation detector, and the position-orientation-scale detector. As a contribution of this paper, we perform thorough comparison between MSL and FSL. Experiments show MSL significantly outperforms FSL on both the training and test sets. Additionally, we also detect several LV landmarks, such as the LV apex and two annulus points. If we combine the detected candidates from both the whole-object detector and landmark detectors, we can further improve the system robustness even when one detector fails. A novel ranking-based strategy is proposed to combine the detected candidates from all detectors. Experiments show our ranking-based aggregation approach can significantly reduce the detection outliers.
image classification, robust object detection, marginal space learning, ranking-based multidetector aggregation, left ventricle detection, 2D MRI image, magnetic resonance imaging, position detector, position-orientation detector, position-orientation-scale detector, full space learning, outlier detection
Xiaoguang Lu, B. Georgescu, A. Littmann, E. Mueller, D. Comaniciu, "Robust object detection using marginal space learning and ranking-based multi-detector aggregation: Application to left ventricle detection in 2D MRI images", CVPR, 2009, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition 2009, pp. 1343-1350, doi:10.1109/CVPRW.2009.5206808
5 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool