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Type: Conference paper
Title: A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure
Author: Zhou, S.
Guo, F.
Park, J.
Carneiro, G.
Jackson, J.
Simopoulos, C.
Brendel, M.
Otsuki, J.
Comaniciu, D.
Citation: 11th IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 14-20, 2007 / pp.1-8
Publisher: IEEE
Publisher Place: USA
Issue Date: 2007
ISBN: 9781424416318
Conference Name: IEEE International Conference on Computer Vision (11th : 2007 : Rio de Janeiro, Brazil)
Statement of
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.
Rights: ©2007 IEEE
RMID: 0020114280
DOI: 10.1109/ICCV.2007.4409045
Appears in Collections:Computer Science publications

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