title: Subvoxel accurate graph search using non-Euclidean graph space.
abstract: Graph search is attractive for the quantitative analysis of volumetric medical images, and especially for layered tissues, because it allows globally optimal solutions in low-order polynomial time. However, because nodes of graphs typically encode evenly distributed voxels of the volume with arcs connecting orthogonally sampled voxels in Euclidean space, segmentation cannot achieve greater precision than a single unit, i.e. the distance between two adjoining nodes, and partial volume effects are ignored. We generalize the graph to non-Euclidean space by allowing non-equidistant spacing between nodes, so that subvoxel accurate segmentation is achievable. Because the number of nodes and edges in the graph remains the same, running time and memory use are similar, while all the advantages of graph search, including global optimality and computational efficiency, are retained. A deformation field calculated from the volume data adaptively changes regional node density so that node density varies with the inverse of the expected cost. We validated our approach using optical coherence tomography (OCT) images of the retina and 3-D MR of the arterial wall, and achieved statistically significant increased accuracy. Our approach allows improved accuracy in volume data acquired with the same hardware, and also, preserved accuracy with lower resolution, more cost-effective, image acquisition equipment. The method is not limited to any specific imaging modality and readily extensible to higher dimensions.
authors.affiliation: Department of Ophthalmology and Visual Sciences, Stephen A Wynn Institute for Vision Research, Department of Biomedical Engineering, and Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America; Iowa City Veterans Administration Medical Center, Iowa City, Iowa, United States of America.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abràmoff
authors.affiliation: Department of Electrical and Computer Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Kyungmoo
authors.initials: K
authors.lastname: Lee
authors.affiliation: Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Li
authors.initials: L
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D000792:Angiography; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D012160:Retina / Q000000981:diagnostic imaging; D041623:Tomography, Optical Coherence