TY - GEN
T1 - Simultaneous detection and registration for Ileo-Cecal valve detection in 3D CT Colonography
AU - Lu, Le
AU - Barbu, Adrian
AU - Wolf, Matthias
AU - Liang, Jianming
AU - Bogoni, Luca
AU - Salganicoff, Marcos
AU - Comaniciu, Dorin
PY - 2008
Y1 - 2008
N2 - Object detection and recognition has achieved a significant progress in recent years. However robust 3D object detection and segmentation in noisy 3D data volumes remains a challenging problem. Localizing an object generally requires its spatial configuration (i.e., pose, size) being aligned with the trained object model, while estimation of an object's spatial configuration is only valid at locations where the object appears. Detecting object while exhaustively searching its spatial parameters, is computationally prohibitive due to the high dimensionality of 3D search space. In this paper, we circumvent this computational complexity by proposing a novel framework capable of incrementally learning the object parameters (IPL) of location, pose and scale. This method is based on a sequence of binary encodings of the projected true positives from the original 3D object annotations (i.e., the projections of the global optima from the global space into the sections of subspaces). The training samples in each projected subspace are labeled as positive or negative, according their spatial registration distances towards annotations as ground-truth. Each encoding process can be considered as a general binary classification problem and is implemented using probabilistic boosting tree algorithm. We validate our approach with extensive experiments and performance evaluations for Ileo-Cecal Valve (ICV) detection in both clean and tagged 3D CT colonography scans. Our final ICV detection system also includes an optional prior learning procedure for IPL which further speeds up the detection.
AB - Object detection and recognition has achieved a significant progress in recent years. However robust 3D object detection and segmentation in noisy 3D data volumes remains a challenging problem. Localizing an object generally requires its spatial configuration (i.e., pose, size) being aligned with the trained object model, while estimation of an object's spatial configuration is only valid at locations where the object appears. Detecting object while exhaustively searching its spatial parameters, is computationally prohibitive due to the high dimensionality of 3D search space. In this paper, we circumvent this computational complexity by proposing a novel framework capable of incrementally learning the object parameters (IPL) of location, pose and scale. This method is based on a sequence of binary encodings of the projected true positives from the original 3D object annotations (i.e., the projections of the global optima from the global space into the sections of subspaces). The training samples in each projected subspace are labeled as positive or negative, according their spatial registration distances towards annotations as ground-truth. Each encoding process can be considered as a general binary classification problem and is implemented using probabilistic boosting tree algorithm. We validate our approach with extensive experiments and performance evaluations for Ileo-Cecal Valve (ICV) detection in both clean and tagged 3D CT colonography scans. Our final ICV detection system also includes an optional prior learning procedure for IPL which further speeds up the detection.
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U2 - 10.1007/978-3-540-88693-8_34
DO - 10.1007/978-3-540-88693-8_34
M3 - Conference contribution
AN - SCOPUS:56749153963
SN - 3540886923
SN - 9783540886921
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 465
EP - 478
BT - Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PB - Springer Verlag
T2 - 10th European Conference on Computer Vision, ECCV 2008
Y2 - 12 October 2008 through 18 October 2008
ER -