Active spline model: A shape based modelinteractive segmentation
We propose to formulate point distribution model in terms of centripetal-parameterized CatmullRom spline, so that the model-based segmentation is augmented to permit quick edit, and the consequent shape is independent of scale. We train the model in a fashion similar to active shape model, but with fewer salient/landmark points. We use gradient vector flow field as the external force field to drive the segmentation, but we did not adopt the procedures panned out by Cootes et al. to update a shape. Instead, we transform the shape back and forth between model scale and image scale to get the shape converged to the object of interest. To test the method, we turned the solution into an automated algorithm to segment lung on chest radiographs, and achieved an average overlap of 0.879. With edit, the average overlap increased to 0.945, with a minimum of 0.925. The method can be applied on a variety of images, as illustrated in Appendix C. The source code of the algorithm and the demo video can be located at http://jenh.co/2014/01/09/active-spline-models/.
active spline models point distribution model catmullrom spline model-based segmentation