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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.
Date of Conference: 20-25 June 2009