Cardiovascular Informatics

  • I. A. KakadiarisEmail author
  • U. Kurkure
  • A. Bandekar
  • S. O’Malley
  • M. Naghavi


As cardiac imaging technology advances, large amounts of imaging data are being produced which are not being mined sufficiently by current diagnostic tools for early detection and diagnosis of cardiovascular disease. We aim to develop a computational framework to mine cardiac imaging data and provide quantitative measures for developing a new risk assessment method. In this chapter, we present novel methods to quantify pericardial fat in non-contrast cardiac computed tomography images automatically, and to detect and quantify neovascularization in the coronary vessels using intra-vascular ultrasound imaging.


Heart Attack Vulnerable Plaque Image Analysis Method Probabilistic Atlas Frame Gating 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank all members of the Ultimate IVUS team for their valuable assistance. This work was supported in part by NSF Grant IIS-0431144 and an NSF Graduate Research Fellowship (SMO). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • I. A. Kakadiaris
    • 1
    Email author
  • U. Kurkure
    • 1
  • A. Bandekar
    • 1
  • S. O’Malley
    • 1
  • M. Naghavi
    • 2
  1. 1.CBLUniversity of HoustonHoustonTX
  2. 2.AEHAHoustonTX

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