Three-dimensional seismic data has been used to explore the earth's crust for over 30 years, yet the identification of geologic features in the migrated seismic image remains a time consuming manual task. Current approaches fail to realistically model many 3D geologic features and offer no integrated segmentation capabilities. In the image processing community, image structure analysis techniques have demonstrated encouraging results through filters that enhance feature structure using partial derivative information. These techniques are only beginning to be applied to the field of seismic interpretation and the information they generate remains to be explored for feature segmentation. Dynamic implicit surfaces, implemented with level set methods, have shown great potential in the computational sciences for applications such as modeling, simulation, and segmentation. Level set methods allow implicit handling of complex topologies deformed by operations where large changes can occur without destroying the level set representation. Many real-world objects can be represented as an implicit surface but further interpretation of those surfaces is often severely limited, such as the growth and segmentation of planelike and high positive curvature features. In addition, the complexity of many evolving surfaces requires visual monitoring and user control in order to achieve preferred results.