Abstract-In this paper, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via subband filtering using an orientation pyramid (Wilson and Spann, 1988). A novel Bhattacharyya space, based on the Bhattacharyya distance, is proposed for selecting the most discriminant measurements and producing a compact feature space. An oct tree is built of the multivariate features space and a chosen level at a lower spatial resolution is first classified. The classified voxel labels are then projected to lower levels of the tree where a boundary refinement procedure is performed with a three-dimensional (3-D) equivalent of butterfly filters. The algorithm was tested with 3-D artificial data and three magnetic resonance imaging sets of human knees with encouraging results. The regions segmented from the knees correspond to anatomical structures that can be used as a starting point for other measurements such as cartilage extraction.
In REF , Reyes-Aldasoro and Bhalerao propose characterising volumetric textures by extracting textural measurements from the Fourier domain via subband filtering using an oriented pyramid.
Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging
Computer Science Medicine Mathematics