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Proceedings Paper

Automatic left ventricle detection in MRI images using marginal space learning and component-based voting
Author(s): Yefeng Zheng; Xiaoguang Lu; Bogdan Georgescu; Arne Littmann; Edgar Mueller; Dorin Comaniciu
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Paper Abstract

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 orientation, size, shape, and image intensity of the LV, automatic detection of the LV is still a challenging problem. In this paper, we propose to use marginal space learning (MSL) to exploit the recent advances in learning discriminative classifiers. Instead of learning a monolithic classifier directly in the five dimensional object pose space (two dimensions for position, one for orientation, and two for anisotropic scaling) as full space learning (FSL) does, we train three detectors, namely, the position detector, the position-orientation detector, and the position-orientation-scale detector. Comparative experiments show that MSL significantly outperforms FSL in both speed and accuracy. 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. A novel voting based strategy is devised to combine the detected candidates by all detectors. Experiments show component-based voting can reduce the detection outliers.

Paper Details

Date Published: 27 March 2009
PDF: 12 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725906 (27 March 2009); doi: 10.1117/12.811045
Show Author Affiliations
Yefeng Zheng, Siemens Corporate Research (United States)
Xiaoguang Lu, Siemens Corporate Research (United States)
Bogdan Georgescu, Siemens Corporate Research (United States)
Arne Littmann, Siemens Healthcare (Germany)
Edgar Mueller, Siemens Healthcare (Germany)
Dorin Comaniciu, Siemens Corporate Research (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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