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Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images
- Source :
- Journal of biomedical optics. 21(7)
- Publication Year :
- 2016
-
Abstract
- We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed “adjusted mean arc length” (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra’s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.
- Subjects :
- genetic structures
Computer science
Research Papers: Imaging
Biomedical Engineering
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
01 natural sciences
030218 nuclear medicine & medical imaging
010309 optics
Biomaterials
03 medical and health sciences
Macular Degeneration
0302 clinical medicine
Optics
Optical coherence tomography
0103 physical sciences
medicine
Humans
Segmentation
medicine.diagnostic_test
business.industry
Graph theory
Pattern recognition
Image segmentation
Atomic and Molecular Physics, and Optics
eye diseases
Electronic, Optical and Magnetic Materials
Computer Science::Computer Vision and Pattern Recognition
Shortest path problem
Graph (abstract data type)
Artificial intelligence
sense organs
business
Dijkstra's algorithm
Algorithms
Tomography, Optical Coherence
Subjects
Details
- ISSN :
- 15602281
- Volume :
- 21
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- Journal of biomedical optics
- Accession number :
- edsair.doi.dedup.....667bba4bd616f7166650108c6ca66a30