3D adaptive model-based segmentation of human vessels

We introduce an adaptive model fitting approach for the segmentation of vessels from 3D tomographic images. With this approach the shape and size of the 3D region-of-interest (ROI) used for model fitting are automatically adapted to the local width, curvature, and orientation of a vessel to increase the robustness and accuracy. The approach uses a 3D cylindrical model and has been successfully applied to segment human vessels from 3D MRA image data. Our experiments show that the new adaptive scheme yields superior segmentation results in comparison to using a fixed size ROI. Moreover, a validation of the approach based on ground-truth provided by a radiologist confirms its accuracy. In addition, we also performed an experimental comparison of the new approach with a previous scheme.