Abstract: MRI and CT images are commonly used for disease detection. Another method used in this area – Positron Emission Tomography (PET) for image analysis. For clinical PET scanning, positron emission radioactive isotopes are injected into the human body. The following are the major drawbacks of this type of scanning: (1) radioactive isotopes have side effects on patients and (2) PET scan is expensive. For these two reasons, PET simulators are needed for physical and clinical research. This work proposes a new method to simulate the PET image of the brain with Monte Carlo simulation in Matlab. For the simulation, MRI and CT based, segmented image, is used as the original image. In order to produce a correct segmentation of MR Images the intensity non - uniformity (INU) artifact needs to be modelled and compensated. Adaptive Spatial Fuzzy Center Means segmentation is used for brain tissue segmentation of MRI. It is based on Fuzzy Center Means that address both INU artifact and local spatial continuity.

Keywords: INU artifact, FCM, ASFCM, membership values, clusters, fuzziness factor