Variational curve skeletons using gradient vector flow.
Representing a 3D shape by a set of 1D curves that are locally symmetric with respect to its boundary (i.e., curve skeletons) is of importance in several machine intelligence tasks. This paper presents a fast, automatic, and robust variational framework for computing continuous, subvoxel accurate curve skeletons from volumetric objects. A reference point inside the object is considered a point source that transmits two wave fronts of different energies. The first front (beta-front) converts the object into a graph, from which the object salient topological nodes are determined. Curve skeletons are tracked from these nodes along the cost field constructed by the second front (alpha-front) until the point source is reached. The accuracy and robustness of the proposed work are validated against competing techniques as well as a database of 3D objects. Unlike other state-of-the-art techniques, the proposed framework is highly robust because it avoids locating and classifying skeletal junction nodes, employs a new energy that does not form medial surfaces, and finally extracts curve skeletons that correspond to the most prominent parts of the shape and hence are less sensitive to noise.
Hassouna
M Sabry
MS
Vital Images Inc, Eden Prairie, MN 55344, USA. msabry@cvip.uofl.edu
Farag
Aly A
AA
D000818:Animals; D001185:Artificial Intelligence; D001842:Bone and Bones / Q000033:anatomy & histology*; D003196:Computer Graphics; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated