Dynamic PET Image Segmentation Using Multiphase Level Set Method

Image segmentation plays an important role in medical diagnosis. Most existing segmentation methods are focused on 2-D or 3-D images. Here we propose an image segmentation method for 4-D dynamic PET images. We consider that voxels inside each organ have similar time activity curves. The use of tracer dynamic information allows us to separate regions that have similar integrated activity in a static image but with different temporal responses. We develop a multi-phase level set method (MP-LSM) that utilizes both the spatial and temporal information in a dynamic PET data set. Different weighting factors are assigned to each image frame based on the noise level. We used a weighted absolute difference function in the data matching term to increase the robustness of the estimate and to avoid over-partition of regions with high contrast. The proposed method can be applied to both dynamic and static PET images, as well as coregistered images from dual modality imaging systems, such as PET/CT. We validated the proposed method using computer simulated dynamic PET data, as well as real mouse data from a microPET scanner, and compared the results to that of a dynamic clustering method. The results show that the proposed method results in cleaner and smoother segments and less misclassified voxels.