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|Title:||A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure|
|Citation:||11th IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 14-20, 2007 / pp.1-8|
|Conference Name:||IEEE International Conference on Computer Vision (11th : 2007 : Rio de Janeiro, Brazil)|
|S. Kevin Zhou, F. Guo, J.H. Park, G. Carneiro, J. Jackson, M. Brendel, C. Simopoulos, J. Otsuki, and D. Comaniciu|
|Abstract:||We propose a probabilistic, hierarchical, and discriminant (PHD) framework for fast and accurate detection of deformable anatomic structures from medical images. The PHD framework has three characteristics. First, it integrates distinctive primitives of the anatomic structures at global, segmental, and landmark levels in a probabilistic manner. Second, since the configuration of the anatomic structures lies in a high-dimensional parameter space, it seeks the best configuration via a hierarchical evaluation of the detection probability that quickly prunes the search space. Finally, to separate the primitive from the background, it adopts a discriminative boosting learning implementation. We apply the PHD framework for accurately detecting various deformable anatomic structures from M- mode and Doppler echocardiograms in about a second.|
|Appears in Collections:||Computer Science publications|
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