Open AccessRetina layer segmentation using kernel graph cuts and continuous max-flow
Author(s) -
Djibril Kaba,
Y. Wang,
C. Wang,
X. Liu,
Haogang Zhu,
Ana G. Salazar-Gonzalez,
Y. Li
Publication year - 2015
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.007366
Subject(s) - segmentation , optical coherence tomography , retinal , computer science , sørensen–dice coefficient , pixel , artificial intelligence , nerve fiber layer , optic disc , computer vision , mean squared error , optic disk , optics , image segmentation , pattern recognition (psychology) , mathematics , ophthalmology , physics , medicine , statistics
Circular scan Spectral-Domain Optic Coherence Tomography imaging (SD-OCT) is one of the best tools for diagnosis of retinal diseases. This technique provides more comprehensive detail of the retinal morphology and layers around the optic disc nerve head (ONH). Since manual labelling of the retinal layers can be tedious and time consuming, accurate and robust automated segmentation methods are needed to provide the thickness evaluation of these layers in retinal disorder assessments such as glaucoma. The proposed method serves this purpose by performing the segmentation of retinal layers boundaries in circular SD-OCT scans acquired around the ONH. The layers are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. Results from scan images acquired with Spectralis (Heidelberg Engineering, Germany) prove that the proposed method is robust and efficient in detecting the retinal layers boundaries in images. With a mean root-mean-square error (RMSE) of 0.0835 ± 0.0495 and an average Dice coefficient of 0.9468 ± 0.0705 pixels for the retinal nerve fibre layer thickness, the proposed method demonstrated effective agreement with manual annotations.