Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering

Segmentation of layers in retinal images obtained by optical coherence tomography (OCT) has become an important clinical tool to diagnose ophthalmic diseases. However, due to the susceptibility to speckle noise and shadow of blood vessels etc., the layer segmentation technology based on a single ima...

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Bibliographic Details
Published inJournal of innovative optical health science Vol. 14; no. 1; pp. 2140011-1 - 2140011-11
Main Authors Ma, Yushu, Gao, Yingzhe, Li, Zhaolin, Li, Ang, Wang, Yi, Liu, Jian, Yu, Yao, Shi, Wenbo, Ma, Zhenhe
Format Journal Article
LanguageEnglish
Published Singapore World Scientific Publishing Company 01.01.2021
World Scientific Publishing Co. Pte., Ltd
World Scientific Publishing
Subjects
automatic segmentation
Blood vessels
Boundaries
Filtration
Identification methods
Image processing
Image segmentation
Interpolation
mean filtering
Medical imaging
Noise
Optical Coherence Tomography
Research Article
Retina
Retinal images
retinal layers
Signal to noise ratio
Tomography
Optical coherence tomography
automatic segmentation
mean filtering
retinal layers
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Summary:Segmentation of layers in retinal images obtained by optical coherence tomography (OCT) has become an important clinical tool to diagnose ophthalmic diseases. However, due to the susceptibility to speckle noise and shadow of blood vessels etc., the layer segmentation technology based on a single image still fail to reach a satisfactory level. We propose a combination method of structure interpolation and lateral mean filtering (SI-LMF) to improve the signal-to-noise ratio based on one retinal image. Before performing one-dimensional lateral mean filtering to remove noise, structure interpolation was operated to eliminate thickness fluctuations. Then, we used boundary growth method to identify boundaries. Compared with existing segmentations, the method proposed in this paper requires less data and avoids the influence of microsaccade. The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects, which successfully identified the boundaries of 10 physiological layers, consistent with the results based on the manual determination.
ISSN:1793-5458
1793-7205
DOI:10.1142/S1793545821400113