Abstract
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.
Original language | English (US) |
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Title of host publication | Handbook of Biomedical Imaging |
Subtitle of host publication | Methodologies and Clinical Research |
Publisher | Springer US |
Pages | 363-374 |
Number of pages | 12 |
ISBN (Electronic) | 9780387097497 |
ISBN (Print) | 9780387097480 |
DOIs | |
State | Published - Jan 1 2015 |
ASJC Scopus subject areas
- General Computer Science
- General Medicine
- General Biochemistry, Genetics and Molecular Biology