Subvoxel accurate graph search using non-Euclidean graph space
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 co...
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Published in | PloS one Vol. 9; no. 10; p. e107763 |
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Main Authors | , , , |
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Language | English |
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14.10.2014
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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. |
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AbstractList | 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. |
Author | Tang, Li Abràmoff, Michael D Wu, Xiaodong Lee, Kyungmoo |
AuthorAffiliation | 1 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 4 Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America 2 Iowa City Veterans Administration Medical Center, Iowa City, Iowa, United States of America Institute of Automation, Chinese Academy of Sciences, China 3 Department of Electrical and Computer Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States of America 5 Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States of America |
AuthorAffiliation_xml | – name: 3 Department of Electrical and Computer Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States of America – name: 4 Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America – name: Institute of Automation, Chinese Academy of Sciences, China – name: 1 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 – name: 5 Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States of America – name: 2 Iowa City Veterans Administration Medical Center, Iowa City, Iowa, United States of America |
Author_xml | – sequence: 1 givenname: Michael D surname: Abràmoff fullname: Abràmoff, Michael D organization: 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 – sequence: 2 givenname: Xiaodong surname: Wu fullname: Wu, Xiaodong organization: Department of Electrical and Computer Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States of America – sequence: 3 givenname: Kyungmoo surname: Lee fullname: Lee, Kyungmoo organization: Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America – sequence: 4 givenname: Li surname: Tang fullname: Tang, Li organization: Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States of America |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25314272$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1016_j_jvcir_2023_103928 crossref_primary_10_1038_srep26559 crossref_primary_10_1016_j_media_2019_02_004 crossref_primary_10_1364_OE_444369 crossref_primary_10_1109_TMI_2018_2890386 |
Cites_doi | 10.1167/iovs.13-11812 10.1167/iovs.09-3790 10.1109/TPAMI.2004.60 10.1016/j.tcs.2010.11.030 10.1109/TMI.2012.2191302 10.1109/TMI.2010.2095870 10.1117/12.843928 10.1109/83.661186 10.1142/S0218195907002331 10.1109/TMI.2012.2225152 10.1109/TMI.2009.2031324 10.1109/TMI.2008.923966 10.1109/TMI.2010.2103566 10.1109/TMI.2002.1000254 10.1109/TMI.2010.2058861 10.1016/j.imavis.2012.10.005 10.1007/3-540-45465-9_88 10.1109/TASSP.1981.1163711 10.1109/RBME.2010.2084567 10.1007/s11263-006-7934-5 10.1109/TPAMI.2006.19 10.1109/34.969114 10.1109/JRPROC.1949.232969 10.1109/TMI.2012.2223760 10.1109/TMI.2009.2016958 10.1109/TMI.2012.2227120 |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors of this manuscript have the following competing interests: Patent application for some of the authors assigned to the University of Iowa from which the authors may benefit (see statement below); Research Grants from NIH, Research to Prevent Blindness, PI Abramoff; American Diabetes Association travel grant to Abramoff; SPIE travel grant to Abramoff; Membership of the American Academy of Ophthalmology (Abramoff); Membership of the Macula Society (Abramoff); and Membership of ARVO (Abramoff, Tang). The patent application is as follows: U.S. Provisional Patent Application Serial No. 61/968,713, filed March 21, 2014. Title: Graph Search Using Non-Euclidean Deformed Graph. Inventors: Abramoff, Tang, and Wu. Filed by and assigned to the University of Iowa, Iowa City, Iowa. The inventors are all employed by the University of Iowa. There are no products or products in development associated with this patent application, nor consultancy. All authors confirm their adherence to all PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. Conceived and designed the experiments: MDA. Performed the experiments: LT KL. Analyzed the data: MDA LT XW. Contributed reagents/materials/analysis tools: MDA KL. Wrote the paper: MDA LT. |
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SubjectTerms | Accuracy Algorithms Angiography Automation Biology and Life Sciences Computer and Information Sciences Computer applications Computer engineering Computing time Data acquisition Equipment costs Euclidean geometry Euclidean space Graph representations Graph theory Humans Image acquisition Image processing Image segmentation Imaging, Three-Dimensional Magnetic Resonance Imaging Medical imaging Medicine and Health Sciences NMR Nodes Nuclear magnetic resonance Optic nerve Optical Coherence Tomography Optics Optimization Physical Sciences Quantitative analysis Research and Analysis Methods Retina Retina - diagnostic imaging Run time (computers) Science Searching Statistical analysis Tissues Tomography Tomography, Optical Coherence |
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Title | Subvoxel accurate graph search using non-Euclidean graph space |
URI | https://www.ncbi.nlm.nih.gov/pubmed/25314272 https://www.proquest.com/docview/1611598069/abstract/ https://search.proquest.com/docview/1612990347 https://pubmed.ncbi.nlm.nih.gov/PMC4196762 https://doaj.org/article/b913765e3eeb4e73b388e69aa60aa8de http://dx.doi.org/10.1371/journal.pone.0107763 |
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