Prior-Constrained Scale-Space Mean Shift

This paper proposes a new variational bound optimization framework for incorporating spatial prior information to the mean shift-based data-driven mode analysis, offering flexible control of the mean shift convergence. Two forms of Gaussian spatial priors are considered. Attractive prior pulls the convergence toward a desired location. Repulsive prior pushes away from such a location. Using a generic variational optimization formulation via construction of quadratic lower and upper bounds, we show that the priorconstrained mean shift step can be interpreted as an information fusion of the data and prior terms in the sense of the best linear unbiased estimator. This approach is used to propose a mode parsing algorithm using the inhibitionof-return principle. The proposed algorithm is used for a semi-automatic 3D segmentation of lung nodules in CT data for evaluating its effectiveness. Our experiments demonstrate that the proposed solution can successfully segment challenging wall-attached cases.