TY  - EJOU
AU  - Huang, Yuxiang  
AU  - He, Chuliu  
AU  - Wang, Jiaqiu  
AU  - Miao, Yuehong  
AU  - Zhu, Tongjin  
AU  - Zhou, Ping  
AU  - Li, Zhiyong  

TI  - Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm
T2  - Molecular \& Cellular Biomechanics

PY  - 2018
VL  - 15
IS  - 2
SN  - 1556-5300

AB  - 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.
KW  - IVOCT
KW  -  image segmentation
KW  -  support vector machine
KW  -  attenuation coefficient
KW  -  image texture features

DO  - 10.3970/mcb.2018.02478