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

Journal article

Huang, Y., He, C., Wang, J., Miao, Y., Zhu, T., Zhou, P. and Li, Z. (2018). Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm. Molecular & Cellular Biomechanics. 15 (2), pp. 117-125. https://doi.org/10.3970/mcb.2018.02478
AuthorsHuang, Y., He, C., Wang, J., Miao, Y., Zhu, T., Zhou, P. and Li, Z.
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.

Year2018
JournalMolecular & Cellular Biomechanics
Journal citation15 (2), pp. 117-125
PublisherTech Science Press
ISSN1556-5300
Digital Object Identifier (DOI)https://doi.org/10.3970/mcb.2018.02478
Publication dates
Print01 Jan 2018
Publication process dates
Deposited08 Jan 2024
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Open
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