Morphological component analysis for automatic segmentation of six lower retina layers in optical coherence tomography images

Abstract

Background: Retina is the innermost tissue in human eye which sends visual information to the brain by means of optic nerve. Designing an intra-retinal layer segmentation method which can detect the retina surfaces properly in the presence of noise and lack of contrast is an important step in ophthalmology. In this study the combination of morphological component analysis (MCA) and dynamic programming (DP) is used automatically for segmentation of optical coherence tomography (OCT) images. Methods: Data set for this study was 55 samples which were taken from normal people by Topcon OCT-1000. This study had two phases. In MCA phase the image dictionary was created by clustering with eigenvalues (k-SVD), and then the image was decomposed to cartoon and texture parts by selecting proper bases. In the second phase segmentation was done by the dynamic programming (DP) method on cartoon part and the retinal pigment epithelium (RPE), Verhoeff's memberane (VM), outer segment layer (OSL), inner collagenous layer (ICL), inner synaptic layer (ISL), and outer limiting membrane (OLM) layers were detected. Findings: Comparing the obtained results with gold standard (manual segmentation) shows that minimum error belongs to OSL surface and its error in the form of mean ± SD (standard derivation) is 0.030 ± 0.167. For other surfaces the error is calculated in this way from left to right for RPE, VM, ICL, ISL, OLM:-0.66 ± 0.33, -0.59 ± 0.31, -1.00 ± 0.49, -1.72 ± 0.61, -1.05 ± 0.51. Conclusion: MCA in combination with DP can work as an automatic method for six lower intra retina layers' segmentation with acceptable accuracy. One of the main advantages of this method is omitting preprocessing phase for segmentation. © 2016, Journal of Isfahan Medical School. All rights reserved.