Comparison of 2D and 3D region-based deformable models and random walker methods for PET segmentation

In this paper, we propose to compare different methods for tumor segmentation in positron emission tomography (PET) images. We first propose to tackle this problem under the umbrella of shape optimization and 3D deformable models. Indeed, 2D active contours have been widely investigated in the literature but these techniques do not take advantage of 3D informations. On the one hand, we use the well-known model of Chan and Vese. On the other hand we use a criterion based on parametric probabilities which allows us to test the assumption of Poisson distribution of the intensity in such images. Both will be compared to their 2D equivalent and to an improved random-walker algorithm. For this comparison, we use a set of simulated, phantom and real sequences with a known ground-truth and compute the corresponding Dice Coefficients. We also give some examples of 2D and 3D segmentation results.