![43543-Yue, Yong 43543-Yue, Yong](https://mc-34647c8d-0ad3-4e6c-832a-7092-cdn.azureedge.net/-/media/project/facultyprofileimages/2021/02/06/14/24/43543-yong-yue.png?rev=786318e72ead45ea9b94f4b611d414ee&la=en&h=180&w=120&hash=E4A538B6BAC681B96B644D5B10618B6C)
Yong Yue, PhD
Associate Professor of Clinical Radiation Oncology
- yongyue@iu.edu
- Phone
- (317) 962-3172
- Address
-
535 Barnhill Drive, Suite RT 041
RAON
Indianapolis, IN 46202
Year | Degree | Institution |
---|---|---|
2007 | PhD | Rice University |
1996 | BS | Lanzhou University |
Treatment planning systems: Varian Eclipse
Information systems: Varian AIRA Oncology Information System and Elekta MOSAIQ
Linear accelerators: Varian TrueBeam and Trilogy, Elekta Versa HD
HDR afterloaders: Varian Varisource iX
CT simulators: GE Discovery CT with the Varian RPM system
Water tank system: IBA Blue Phantom 2 with the OmniPro Accept 7.4.
Breathing monitoring systems: Varian RPM system and Calypso 4D system, Vision RT, QFix SDX system
Breathing motion simulation and QA phantoms: Dynamic Thorax Phantom
QA devices: Sun Nuclear ArcCheck, Mapcheck2, IC profiler, IBA MatriXX Evolution, Landauer nanoDot OSLD system,
and the Phantom Laboratory RANDO phantom
Software: Varian Eclipse API, Sun Nuclear SNC Patient, and Varian Velocity, MIM software, RadCal, MATLAB, SAS
Languages: C/C++/C#, Cuda GPU programming, Perl, Python, R
Medical image analysis and processing
- Multiple imaging modality fusion: rigid and non-rigid registration, B-spline deformable registration, optical flow,
demons and feature-based elastic body registration
- Image segmentation: Anatomical structure boundary detection using active contour, level set, deformable models,
geodesic active contour, active shape model, maximum a posteriori segmentation, and learning based segmentation
- Image reconstruction: Filter back projection (FBP), ordered-subsets expectation-maximization (OSEM)
- Computational modeling: Statistical image modeling, machine learning, and pattern recognition, statistical learning,
nonlinear discriminant analysis, manifold learning