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Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm

Yuxiang Huang1, Chuliu He1, Jiaqiu Wang2, Yuehong Miao1, Tongjin Zhu1, Ping Zhou1, Zhiyong Li1,2,*
School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia.
* Corresponding author: Zhiyong Li. Email: .

Molecular & Cellular Biomechanics 2018, 15(2), 117-125. https://doi.org/ 10.3970/mcb.2018.02478

Abstract

Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and 86.5% lipid tissue. The results show that this method can be a considerable way for semi-automatic segmentation of atherosclerotic plaque components in clinical IVOCT images.

Keywords

IVOCT, image segmentation, support vector machine, attenuation coefficient, image texture features.

Cite This Article

Huang, Y., He, C., Wang, J., Miao, Y., Zhu, T. et al. (2018). Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm. Molecular & Cellular Biomechanics, 15(2), 117–125.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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