Research >> Feature extraction
Feature extraction
Our feature extraction algorithms "trace" out the
vascular structure
of the retina. The earliest algorithm, developed
by Professor Roysam's
group, traces using a two-sided edge model.
This has been extended to
accurate measurement of vessel width. A
more recent algorithm is built
on a more sophisticated model of the
appearance of a vesselness.
Faculty: Chuck Stewart 
Current Students: Michal Sofka 
Former Students: Ken Fritzche 
Project website and executable:
http://www.vision.cs.rpi.edu/vessels/
Publications:
M. Sofka and C.V. Stewart, "Retinal Vessel Extraction Using Multiscale
Matched Filters, Confidence and Edge Measures",
IEEE Transactions on Medical Imaging, vol. 25, no. 12, pp. 1531-1546, Dec. 2006.
[ pdf ] [ bib ]
K. Fritzsche, A. Can, H. Shen, C. Tsai, J. Turner, H. Tanenbuam, C. Stewart,
and B. Roysam.
Automated model based segmentation, tracing and analysis
of retinal vasculature from digital fundus
images. In J. S. Suri and S. Laxminarayan,
editors, State-of-The-Art Angiography, Applications and
Plaque Imaging Using
MR, CT, Ultrasound and X-rays, pages 225–298. Academic Press, 2003. [ bib ]
K. Fritzsche. Computer Vision Algorithms for Retinal Vessel Detection and Width
Change Detection.
PhD thesis, Rensselaer Polytechnic Institute, Troy, New York,
Dec 2004.
A. Can, H. Shen, J. N. Turner, H. L. Tanenbaum, and B. Roysam. Rapid automated
tracing and
feature extraction from live high-resolution retinal fundus images using
direct exploratory algorithms. IEEE Transactions on Information Technology in
Biomedicine, 3(2):125–138, 1999.
[ pdf ] [ bib ]
Home | People | Research | Publications | Contact | News/Events | Download | Links