Volumetric analysis is the process of extracting useful information from three-dimensional data sets. Two areas of interest are volumetric segmentation and interest detection. Volumetric segmentation can be a difficult problem, particularly when type of structures to expect is unknown. A computationally inexpensive algorithm for segmenting large volumes with minimal a priori knowledge is presented. Interest detection is an essential step for identifying salient regions an a volume. A transform for characterizing potentially interesting regions is presented, along with a larger interest detection framework.