Open Access

ARTICLE

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 Li^1,2,*

1 School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
2 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.

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Keywords

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