3D Robust Adaptive Region Growing for segmenting [18F] fluoride ion PET images

We propose a new robust adaptive region growing method (RoAd RG) based on two local parameters: the local mean value of the intensity function and the local mean value of the norm of the intensity gradient. This approach enables a better spread of the region growing inside the region of interest while avoiding the merge of outlier pixels. We tested our method on a synthesized noisy image, and demonstrated that RoAd RG gives better result than non adaptive or not fully adaptive methods. We applied positively our method to 3D [18F]fluoride ion PET images for segmenting bone structures, and showed its superiority compared to a non adaptive method.