Delineation of brain structures from positron emission tomography images with deformable models

Segmentation of positron emission tomography (PET) images is a difficult task. In this study, we propose a new method for delineation of brain structures according to the tracer uptake. The method is based on a new deformable model which is particularly designed for extracting surfaces automatically from noisy images. The automation is achieved by using a global optimization algorithm for minimizing the energy of the deformable model. As an example, the coarse cortical structure was extracted from FDG PET brain images by delineating first the brain surface and then the white matter surface. We have tested the method with the image of the brain phantom and images from a small number (N = 17) of FDG brain studies. The cortical structure was automatically and reliably found from all the images. The proposed method provides new opportunities for automatic and repeatable structure extraction applicable for regional quantification of the tracer uptake.