Kinetic modeling based probabilistic segmentation for molecular images.
We propose a semi-supervised, kinetic modeling based segmentation technique for molecular imaging applications. It is an iterative, self-learning algorithm based on uncertainty principles, designed to alleviate low signal-to-noise ratio (SNR) and partial volume effect (PVE) problems. Synthetic fluorodeoxyglucose (FDG) and simulated Raclopride dynamic positron emission tomography (dPET) brain images with excessive noise levels are used to validate our algorithm. We show, qualitatively and quantitatively, that our algorithm outperforms state-of-the-art techniques in identifying different functional regions and recovering the kinetic parameters.
Saad
Ahmed
A
Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada. aasaad@cs.sfu.ca
Hamarneh
Ghassan
G
Möller
Torsten
T
Smith
Ben
B
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D019788:Fluorodeoxyglucose F18; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D015336:Molecular Probe Techniques; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18