A preliminary study of IVOCT-based atherosclerosis plaque classification technique

Rajkumar, Sanjiv and Soaib, Muhammad Safwan and Liew, Yih Miin and Chee, Kok Han and Tang, Ho Kin and Naidu, Kanendra and Arifin, Nooranida and Chan, Chow Khuen (2022) A preliminary study of IVOCT-based atherosclerosis plaque classification technique. In: 6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021, 28-29 July 2021, Virtual, Online.

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Abstract

Atherosclerosis is a type of cardiovascular disease (CVD) that affects the coronary artery by build-up of plaque, which can potentially cause stroke or ischemic damage to the surrounding tissue. Intravascular Optical Coherence Tomography (IVOCT), an imaging modality, is able to capture detailed images of arteries affected by atherosclerosis that contain identifiable characteristics. These characteristics can assist clinicians to differentiate certain plaque types such as, fibrous, calcific and lipid, and provide diagnosis appropriately. However, clinicians face challenges in manual visual plaque identification from IVOCT images such as fatigue and IVOCT artifacts. Hence, the aim of this study is to produce an automated IVOCT-based plaque segmentation method to assist clinicians in their diagnosis. This preliminary study investigated only two plaque types, which are fibrous and calcified plaque as they are much more prominent to be labelled manually. The image dataset was pre-processed with Gabor filters before training the Random Forest (RF) and XGBoost models. The results demonstrated that the XGBoost model performed slightly better than the Random Forest model with 82.0 and 80.9 accuracy respectively. This shows that machine learning techniques can be applied conveniently to assist, automate and reduce the time for clinician’s visual assessment in the overall diagnosis workflow. © 2022, Springer Nature Switzerland AG.

Item Type: Conference or Workshop Item (Paper)
Funders: Malaysia Ministry of Higher Education Fundamental Research Grant Scheme [Grant no. FRGS/1/2018/SKK03/UM/02/1, GPF026A-2019]
Uncontrolled Keywords: Atherosclerosis; Image segmentation; Intravascular optical coherence tomography; Machine learning; Plaque classification
Subjects: R Medicine > R Medicine (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Biomedical Engineering
Faculty of Medicine > Medicine Department
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 30 May 2025 03:00
Last Modified: 30 May 2025 03:00
URI: http://eprints.um.edu.my/id/eprint/43434

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