期刊
JOURNAL OF BIOMEDICAL OPTICS
卷 21, 期 10, 页码 -出版社
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JBO.21.10.101407
关键词
optical coherence tomography; image processing; classification; cardiac imaging
资金
- National Institute of Health (NIH) [1DP2HL127776- 01]
- National Science Foundation
- NSF [EEC-1342273, 1454365]
- SPIE educational Scholarship
- Wei foundation fellowship
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1454365] Funding Source: National Science Foundation
Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
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