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Analysis of semantic relations between multimodal medical images based on coronary anatomy for acute myocardial infarction

Title
Analysis of semantic relations between multimodal medical images based on coronary anatomy for acute myocardial infarction
Authors
Park Y.Lee M.Kim M.-H.Lee J.-W.
Ewha Authors
김명희
SCOPUS Author ID
김명희scopus
Issue Date
2016
Journal Title
Journal of Information Processing Systems
ISSN
1976-913XJCR Link
Citation
vol. 12, no. 1, pp. 129 - 148
Keywords
Acute myocardial infarctionCoronary anatomyCoronary angiographyData modelEchocardiographyMedical imagesMultimodalitySemantic features
Publisher
Korea Information Processing Society
Indexed
SCOPUS; KCI scopus
Abstract
Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multimodal medical images with flat and unstructured data. It has a lack of semantic information between multimodal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively. © 2016 KIPS.
DOI
10.3745/JIPS.04.0021
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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