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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 4710305, 11 pages
Research Article

Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set

1The State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi 710119, China
2School of the Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Department of Cardiology, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
5Department of Aerospace Biodynamics, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
6Xidian University, Xi’an, Shaanxi 710071, China

Correspondence should be addressed to Yihui Cao;

Received 31 October 2016; Accepted 11 January 2017; Published 7 February 2017

Academic Editor: Chuangyin Dang

Copyright © 2017 Yihui Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was .