Abstract: Methods, systems, and computer readable media for performing FDG positron emission tomography (PET) quantification, segmentation, and classification of abnormalities are disclosed. One method includes receiving a plurality of magnetic resonance (MR) images corresponding to a target site of a subject and generating three dimensional (3D) area masks of abnormality volumes from the plurality of MR images. The method further includes segmenting the 3D area masks into one or more individual seed images for each of the abnormality volumes and overlaying the one or more individual seed images onto co-registered parametric PET maps to generate kinetic rate parameters for each of the abnormality volumes. The method also includes utilizing the kinetic rate parameters to train a logistic regression engine to predict a target site condition assessment based on a classification of the abnormality volumes.