Automated detection of retinal layer structures on optical coherence tomography images

Abstract

Segmentation of retinal layers from OCT images is fundamental to diagnose the progress of retinal diseases. In this study we show that the retinal layers can be automatically and/or interactively located with good accuracy with the aid of local coherence information of the retinal structure. OCT images are processed using the ideas of texture analysis by means of the structure tensor combined with complex diffusion filtering. Experimental results indicate that our proposed novel approach has good performance in speckle noise removal, enhancement and segmentation of the various cellular layers of the retina using the STRATUSOCTTM system.

Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images

“…In an effort to provide additional retinal quantifications along with accurate automatic/semiautomatic detection, we analyzed the StratusOCT images with a software tool for OCT retinal image analysis (OCTRIMA), which is an interactive, user-friendly standalone application for analyzing StratusOCT retinal images. The OCTRIMA software integrates a novel denoising and edgeenhancement technique along with a segmentation algorithm developed by Cabrera Fernández et al 12 Moreover, OCTRIMA is able to minimize segmentation errors, give quantitative information of intraretinal structures, and also facilitates the analysis of other retinal features that may be of diagnostic and prognostic value, such as morphology and reflectivity. 13 The OCTRIMA software enables the segmentation of seven cellular layers of the retina on OCT images based on their optical densities: the retinal nerve fiber layer, the ganglion cell and inner plexiform layer complex, the inner nuclear layer, the outer plexiform layer, the outer nuclear layer, the inner-outer photoreceptor junction (IS/OS), and retinal pigment epithelium (RPE).…”

A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography

“…Later on, Tan et al using a 2D gradient approach in a dynamic programming framework also confirmed that glaucoma primarily affects the thickness of the inner retinal layers (RNFL, GCL, IPL) in the macula (Tan et al, 2008). Cabrera Fernández et al used complex diffusion filtering to reduce speckle noise without blurring retinal structures and a peak finding algorithm based on local coherence information of the retinal structure to determine seven intraretinal layers in a automatic/semi-automatic framework (Cabrera Fernández et al, 2005b). This algorithm searches for edges in a map obtained by calculating the first derivative of the structure coherence matrix using the denoised image.…”

“…Table 1 includes a summary of all the results that have been presented in the ARVO meetings since 2004. In 2005, algorithms based only on intensity variation were also presented (Shahidi et al, 2005;Ishikawa et al, 2005;Cabrera Fernández et al, 2005b). In general, these algorithms overcame the limitations of the commercial OCT3/Stratus OCT software and also provided additional quantitative information.…”

“…By choosing a different segmentation algorithm (see Fig. 1B), identification of the retinal boundaries can be improved (Cabrera Fernández et al, 2005b). All segmentation methods that have been proposed in the literature aim at improving retinal image segmentation in this or other aspects.…”

Use of Varying Constraints in Optimal 3-D Graph Search for Segmentation of Macular Optical Coherence Tomography Images

“…Because the intraretinal layers may be affected differently by disease, an intraretinal segmentation approach is needed to enable quantification of individual layer properties, such as thickness or texture. While a few reported approaches for macular intraretinal segmentation exist in the literature (e.g., [1]), such approaches have been two-dimensional in nature.…”