@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} }