Int. J. of Biomedical Engineering and Technology   »   2010 Vol.3, No.3/4

 

 

Title: IVUS image processing and semantic analysis for Cardiovascular Diseases risk prediction

 

Author: Charalampos Doulaverakis, Maria Papadogiorgaki, Vasileios Mezaris, Antonis Billis, Eirini Parissi, Ioannis Kompatsiaris, Anastasios Gounaris, Yiannis S. Chatzizisis, George D. Giannoglou

 

Addresses:
Informatics and Telematics Institute, Center for Research and Technology Hellas, 1st km, Thermi-Panorama Road, Thessaloniki 57001, Greece.
Informatics and Telematics Institute, Center for Research and Technology Hellas, 1st km, Thermi-Panorama Road, Thessaloniki 57001, Greece.
Informatics and Telematics Institute, Center for Research and Technology Hellas, 1st km, Thermi-Panorama Road, Thessaloniki 57001, Greece.
Informatics and Telematics Institute, Center for Research and Technology Hellas, 1st km, Thermi-Panorama Road, Thessaloniki 57001, Greece.
Informatics and Telematics Institute, Center for Research and Technology Hellas, 1st km, Thermi-Panorama Road, Thessaloniki 57001, Greece.
Informatics and Telematics Institute, Center for Research and Technology Hellas, 1st km, Thermi-Panorama Road, Thessaloniki 57001, Greece.
Department of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.
Cardiovascular Engineering and Atherosclerosis Laboratory, 1st Cardiology Department, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 1st Kyriakidi Str., GR54636 Thessaloniki, Greece.
Cardiovascular Engineering and Atherosclerosis Laboratory, 1st Cardiology Department, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 1st Kyriakidi Str., GR54636 Thessaloniki, Greece

 

Abstract: The work presented in this paper is part of a system able to perform risk classification of patients based on medical image analysis and on the semantically structured information of patient data from medical records and biochemical data. More specifically, the paper focuses on Intravascular Ultrasound (IVUS) image processing and the automated segmentation developed to extract the useful arterial boundaries. This is coupled with the design and implementation of a semantic reasoning-enabled knowledge base in OWL that integrates data from heterogeneous sources and incorporates functionality for DL classification. Performance evaluation of both IVUS image processing and knowledge base is discussed.

 

Keywords: IVUS image processing; RBF; radial basis function; risk prediction; semantic analysis; OWL; SWRL; intravascular ultrasound; cardiovascular diseases; heart disease; risk classification; medical images; automated segmentation; arterial boundaries.

 

DOI: 10.1504/IJBET.2010.032700

 

Int. J. of Biomedical Engineering and Technology, 2010 Vol.3, No.3/4, pp.349 - 374

 

Available online: 13 Apr 2010

 

 

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