Image segmentation using globally optimal growth in three dimensions with an adaptive feature set
David C. Taylor, William A. Barrett · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
A globally optimal region growing algorithm for 3D segmentation of anatomical objects is developed. The notion of simple 3D connected component labelling is extended to enable the combination of arbitrary features in the segmentation process. This algorithm uses a hybrid octree-btree structure to segment an object of interest in an ordered fashion. This tree structure overcomes the computational complexity of global optimality in three dimensions. The segmentation process is controlled by a set of active features, which work in concert to extract the object of interest. The cost function used to enforce the order is based on the combination of active features. The characteristics of the data throughout the volume dynamically influences which features are active. A foundation for applying user interaction with the object directly to the feature set is established. The result is a system which analyzes user input and neighborhood data and optimizes the tools used in the segmentation process accordingly.