Open AccessAutomated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering
Author(s) -
Yushu Ma,
Yingzhe Gao,
Zhaolin Li,
Ang Li,
Yi Wang,
Jian Liu,
Yao Yu,
Wenbo Shi,
Zhenhe Ma
Publication year - 2021
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545821400113
Subject(s) - optical coherence tomography , speckle noise , artificial intelligence , segmentation , computer vision , interpolation (computer graphics) , computer science , speckle pattern , noise (video) , image segmentation , retinal , pattern recognition (psychology) , optics , image (mathematics) , physics , medicine , ophthalmology
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.