Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Toward Unsupervised Classification of Calcified Arterial Lesions
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Automatic Detection of Calcified Coronary Plaques in Computed Tomography Data Sets
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Detection, grading and classification of coronary stenoses in computed tomography angiography
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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Even with the recent advances in multidetector computed tomography (MDCT) imaging techniques, detection of calcified coronary lesions remains a highly tedious task. Noise, blooming and motion artifacts etc. add to its complication. We propose a novel learning-based, fully automatic algorithm for detection of calcified lesions in contrast-enhanced CT data. We compare and evaluate the performance of two supervised learning methods. Both these methods use rotation invariant features that are extracted along the centerline of the coronary. Our approach is quite robust to the estimates of the centerline and works well in practice. We are able to achieve average detection times of 0.67 and 0.82 seconds per volume using the two methods.