3 research outputs found
Incremental low rank noise reduction for robust infrared tracking of body temperature during medical imaging
Thermal imagery for monitoring of body temperature provides a powerful tool to decrease
health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging).
The presented approach discusses an experiment to simulate radiology conditions with infrared
imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system
uses an incremental low-rank noise reduction applying incremental singular value decomposition
(SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary.
Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal
database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera.
This dataset is created to verify the robustness of our method with respect to motion-artifacts and in
presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the
infrared images in the dataset and was able to successfully measure and track the ROI continuously
(100% detecting and tracking the temperature of participants), and provided considerable robustness
against noise (unchanged accuracy even in 20% additive noise), which shows promising performanc