Cellular automata and anisotropic diffusion filter based interactive tumor segmentation for positron emission tomography
Tumor segmentation in positron emission tomography (PET) aids clinical diagnosis and in assessing treatment response. However, the low resolution and signal-to-noise inherent in PET images, makes accurate tumor segmentation challenging. Manual delineation is time-consuming and subjective, whereas fully automated algorithms are often limited to particular tumor types, and have difficulties in segmenting small and low-contrast tumors. Interactive segmentation may reduce the inter-observer variability and minimize the user input. In this study, we present a new interactive PET tumor segmentation method based on cellular automata (CA) and a nonlinear anisotropic diffusion filter (ADF). CA is tolerant of noise and image pattern complexity while ADF reduces noise while preserving edges. By coupling CA with ADF, our proposed approach was robust and accurate in detecting and segmenting noisy tumors. We evaluated our method with computer simulation and clinical data and it outperformed other common interactive PET segmentation algorithms.