Intra-retinal layer segmentation in optical coherence tomography using an active contour approach
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 0302-9743, Vol: 5762 LNCS, Issue: PART 2, Page: 649-656
2009
- 99Citations
- 77Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations99
- Citation Indexes99
- 99
- CrossRef27
- Captures77
- Readers77
- 77
Conference Paper Description
Optical coherence tomography (OCT) is a non-invasive, depth resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present an automatic segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan-Vese's energy-minimizing active contours without edges for OCT images, which suffer from low contrast and are highly corrupted by noise. We adopt a multi-phase framework with a circular shape prior in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on 20 OCT images from four rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy and average Dice similarity coefficient of 0.85, specifically 0.94 for the the ganglion cell layer, which is the relevant layer for glaucoma diagnosis. © 2009 Springer-Verlag.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=79551681631&origin=inward; http://dx.doi.org/10.1007/978-3-642-04271-3_79; http://www.ncbi.nlm.nih.gov/pubmed/20426167; http://link.springer.com/10.1007/978-3-642-04271-3_79; http://www.springerlink.com/index/10.1007/978-3-642-04271-3_79; http://www.springerlink.com/index/pdf/10.1007/978-3-642-04271-3_79; https://dx.doi.org/10.1007/978-3-642-04271-3_79; https://link.springer.com/chapter/10.1007/978-3-642-04271-3_79
Springer Science and Business Media LLC
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