Subvoxel accurate graph search using non-Euclidean graph space

Michael D Abràmoff; Xiaodong Wu; Kyungmoo Lee; Li Tang

doi:10.1371/journal.pone.0107763

Back

Subvoxel accurate graph search using non-Euclidean graph space

Journal article

Open access

Peer reviewed

Subvoxel accurate graph search using non-Euclidean graph space

Michael D Abràmoff, Xiaodong Wu, Kyungmoo Lee and Li Tang

PloS one, Vol.9(10), e107763

2014

DOI: 10.1371/journal.pone.0107763

PMCID: PMC4196762

PMID: 25314272

Files and links (1)

url

https://doi.org/10.1371/journal.pone.0107763View

Published (Version of record) Open Access

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.

Algorithms

Angiography

Magnetic Resonance Imaging

Retina - diagnostic imaging

Tomography, Optical Coherence

Humans

Imaging, Three-Dimensional

Details

Title: Subtitle: Subvoxel accurate graph search using non-Euclidean graph space
Creators: Michael D Abràmoff - University of Iowa
Xiaodong Wu - Department of Electrical and Computer Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States of America
Kyungmoo Lee - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America
Li Tang - Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States of America
Resource Type: Journal article
Publication Details: PloS one, Vol.9(10), e107763
DOI: 10.1371/journal.pone.0107763
PMID: 25314272
PMCID: PMC4196762
NLM abbreviation: PLoS One
ISSN: 1932-6203
eISSN: 1932-6203
Publisher: United States
Grant note: R01 EY019112 / NEI NIH HHS EY018853 / NEI NIH HHS
Language: English
Date published: 2014
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Ophthalmology and Visual Sciences
Record Identifier: 9983806259602771

Metrics

24 Record Views

8 Times Cited - Web of Science