1 research outputs found
2D PET Image Reconstruction Using Robust L1 Estimation of the Gaussian Mixture Model
An image or volume of interest in positron emission tomography (PET) is
reconstructed from pairs of gamma rays emitted from a radioactive substance.
Many image reconstruction methods are based on estimation of pixels or voxels
on some predefined grid. Such an approach is usually associated with limited
resolution of the reconstruction, high computational complexity due to slow
convergence and noisy results. This paper explores reconstruction of PET images
using the underlying assumption that the originals of interest can be modeled
using Gaussian mixture models. A robust segmentation method based on
statistical properties of the model is presented, with an iterative algorithm
resembling the expectation-maximization algorithm. Use of parametric models for
image description instead of pixels circumvent some of the mentioned
limitations