Fast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images.

Sushil Mittal(1;2), Yefeng Zheng(2), Bogdan Georgescu(2), Fernando Vega-Higuera(3), Shaohua Kevin Zhou(2), Peter Meer(1), Dorin Comaniciu(2)

(1)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA

(2) Siemens Corporate Research, Princeton, NJ 08540, USA

(3) Computed Tomography, Siemens Healthcare, Germany.

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.

International Workshop on Machine Learning in Medical Imaging (MLMI), in conjunction with MICCAI 2010. Beijing, China, September 2010. Springer, Lecture Note in Computer Science LNCS-6357, 1-9.

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