PET-based tumor volume segmentation techniques are under investigation in recent years due to the increased utilization
of FDG-PET imaging in radiation therapy. We have taken the approach of using a Gaussian mixture model (GMM) to model the image intensity distribution of a selected 3D region that completely covers the tumor, called the "analysis region". The modeling is performed with a predetermined number of Gaussian classes and results in a classification of every voxel into one of these classes. The classes are then grouped together to obtain the tumor volume. The only user interaction required is the selection of the "analysis region" and then the algorithm proceeds automatically to initialize the parameters of the different classes and finds the maximum likelihood estimate with expectation maximization. We used 13 clinical and 19 phantom cases to evaluate the precision and accuracy of the segmentation. Reproducibility was within 10% of the average tumor volume estimate and accuracy was ±35% of the true tumor volume and better when compared to two other proposed techniques. The GMM segmentation is extremely user friendly with good precision and accuracy. It has shown great potential to be used in the clinical environment.