Supervoxel-Based Segmentation of 3D Volumetric Images

While computer vision has made noticeable advances in the state of the art for 2D image segmentation, the same cannot be said for 3D volumetric datasets. In this work, we present a scalable approach to volumetric segmentation. The methodology, driven by supervoxel extraction, combines local and global gradient-based features together to first produce a low level supervoxel graph. Subsequently, an agglomerative approach is used to group supervoxel structures into a segmentation hierarchy with explicitly imposed containment of lower level supervoxels in higher level supervoxels. Comparisons are conducted against state of the art 3D segmentation algorithms. The considered applications are 3D spatial and 2D spatiotemporal segmentation scenarios.