Optimized spectral clustering for segmentation of dynamic PET images

The quantification of dynamic PET images requires the definition of regions of interest. The manual delineation is a time consuming and unreproducible process due to the poor resolution of PET images. Approaches were proposed in the literature to classify the kinetic profiles of voxels, however, they are generally either sensitive to initial conditions or favor convex shaped clusters. Recently we have proposed a kinetic spectral clustering (KSC) method for segmentation of dynamic PET images that has the advantage of handling clusters with arbitrary shape in the space in which they are identified. However, its use for clinical applications is still hindered by the manual setting of several parameters. In this paper, we propose an extension of KSC to make it automatic (ASC). A new unsupervised clustering criterion is tailored and a global optimization by a probabilistic metaheuristic algorithm is used to select the scale parameter and the weighting factors involved in the method. We validate our approach with GATE Monte Carlo simulations. Results obtained with ASC compare closely with those obtained with optimal manual parameterization of KSC, and outperform those obtained with two other approaches from the literature.