Segmentation and Classification Method in IVOCT Images
- 10.2991/jimet-15.2015.60How to use a DOI?
- intravascular; optical coherence tomography; lumen detection; plaque classification
Cardiovascular disease (CVD) is a fatal disease of the heart or blood vessels. Intravascular optical coherence tomography (IVOCT) as a newly emerging optical-based technology can provide real-time, high-resolution, and three dimensional images with micrometer resolution. In this paper, an automatic lumen detection method composed of OSTU threshold and active contour model, was investigated to improve the robustness and accuracy. The proposed method is compared with manual lumen detection (MLD), and then average distance and max distance results are obtained. For the given datasets, the average distance and max distance is 0.020mm and 0.088mm respectively. Furthermore, an automatic plaque segmentation and classification is proposed to use Hidden Markov Models(HMM), GLCM and Random Forests algorithm. From the color-code plaque classification results, the approach proposed is available. In conclusion, this method can deal with IVOCT image with high robustness and accuracy.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhou Ping AU - Zhu Tongjing AU - Li Zhiyong PY - 2015/12 DA - 2015/12 TI - Segmentation and Classification Method in IVOCT Images BT - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference PB - Atlantis Press SP - 327 EP - 330 SN - 2352-538X UR - https://doi.org/10.2991/jimet-15.2015.60 DO - 10.2991/jimet-15.2015.60 ID - Ping2015/12 ER -