Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/14151
Title: Automated layer segmentation of 3D macular images using hybrid methods
Authors: Wang, C
Wang, Y
Kaba, D
Wang, Z
Liu, X
Li, Y
Issue Date: 2015
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9217 pp. 614 - 628
Abstract: © Springer International Publishing Switzerland 2015.Spectral-Domain Optical Coherence Tomography (SD-OCT) is a non-invasive imaging modality, which provides retinal structures with unprecedented detail in 3D. In this paper, we propose an automated segmentation method to detect intra-retinal layers in OCT images acquired from a high resolution SD-OCT Spectralis HRA+OCT (Heidelberg Engineering, Germany). The algorithm starts by removing all the OCT imaging artifects includes the speckle noise and enhancing the contrast between layers using both 3D nonlinear anisotropic and ellipsoid averaging filers. Eight boundaries of the retinal are detected by using a hybrid method which combines hysteresis thresholding method, level set method, multi-region continuous max-flow approaches. The segmentation results show that our method can effectively locate 8 surfaces for varying quality 3D macular images.
URI: http://bura.brunel.ac.uk/handle/2438/14151
DOI: http://dx.doi.org/10.1007/978-3-319-21978-3_54
ISBN: 9783319219776
ISSN: 0302-9743
1611-3349
Appears in Collections:Dept of Computer Science Research Papers

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