@Article{mcb.2018.02478,
AUTHOR = {Yuxiang  Huang, Chuliu  He, Jiaqiu  Wang, Yuehong  Miao, Tongjin  Zhu, Ping  Zhou, Zhiyong  Li},
TITLE = {Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm},
JOURNAL = {Molecular \& Cellular Biomechanics},
VOLUME = {15},
YEAR = {2018},
NUMBER = {2},
PAGES = {117--125},
URL = {http://www.techscience.com/mcb/v15n2/28617},
ISSN = {1556-5300},
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.},
DOI = {10.3970/mcb.2018.02478}
}