Robust surface registration for brain PET-CT fusion

We propose a robust surface registration using a Gaussian-weighted distance map (GWDM) for PET-CT brain fusion. Our method is composed of four steps. First, we segment the background of PET and CT brain images using 3D seeded region growing and apply inverse operation to the segmented images for getting head without holes. The non-head regions segmented with the head are then removed using the region growing-based labeling and the sharpening filter is applied to the segmented head in order to extract the feature points of the head from PET and CT images, respectively. Second, a GVDM is generated from feature points of CT images to lead the feature points extracted from PET images with large blurry and noisy conditions to robustly align at optimal location onto CT images. Third, similarity measure is evaluated repeatedly by weighted cross-correlation (WCC). In our experiments, we evaluate our method using software phantom and clinical datasets with the aspect of visual inspection, accuracy, robustness, and computational time. In our method, RMSE for translations and rotations are less than 0.1 mm and 0.2 degrees, respectively in software phantom dataset and give better accuracy than the conventional ones. In addition, our method gives a robust registration at optimal location regardless of increasing noise level.