Digital Image Processing
Computers in Biology and Medicine
Multimodality Image Registration Using Spatial Procrustes Analysis and Modified Conditional Entropy
Journal of Signal Processing Systems
Image registration using geometric deformable model and penalized maximum likelihood
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
Robust 3D reconstruction and mean-shift clustering of motoneurons from serial histological images
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
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In this paper, we propose a robust surface registration using a Gaussian-weighted distance map for PET-CT brain fusion. Our method is composed of three steps. First, we segment the head using the inverse region growing and remove the non-head regions segmented with the head using the region growing-based labeling in PET and CT images, respectively. The feature points of the head are then extracted using sharpening filter. Second, a Gaussian-weighted distance map is generated from the feature points of CT images to lead our similarity measure to robust convergence on the optimal location. Third, weighted cross-correlation measures the similarities between the feature points extracted from PET images and the Gaussian-weighted distance map of CT images. In our experiments, we use software phantom and clinical datasets for evaluating our method with the aspect of visual inspection, accuracy, robustness, and computation time. Experimental results show that our method is more accurate and robust than the conventional ones.