Fast automatic detection of calcified coronary lesions in 3D cardiac CT images

Sushil Mittal, Yefeng Zheng, Bogdan Georgescu, Fernando Vega-Higuera, Shaohua Kevin Zhou, Peter Meer, Dorin Comaniciu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings
Pages1-9
Number of pages9
DOIs
StatePublished - 2010
Event1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010 - Beijing, China
Duration: Sep 20 2010Sep 20 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6357 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010
Country/TerritoryChina
CityBeijing
Period9/20/109/20/10

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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