Intra-retinal layer segmentation in optical coherence tomography images

Abstract

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

“…Over the past two decades, the application of image processing and computer vision to OCT image interpretation has mostly focused on the development of automated retinal layer segmentation methods [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Segmented layer thicknesses are compared to the corresponding thickness measurements from normative databases to help identify retinal diseases [19][20][21][22].…”

Multiple-object geometric deformable model for segmentation of macular OCT

“…These eight layers (and their associated nine boundaries) are the maximal set that are typically segmented from OCT of the macular retina. There has been a large body of work on macular retinal OCT layer segmentation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. A diverse array of approaches have been investigated including methods based on active contours [19,20] [31], registration [34], and level sets [35].…”

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

“…Moreover, the algorithm was not evaluated for images including the foveal pit. Later on, Mishra et al also presented a modified active contour algorithm based on a sparse dynamic programming method and a two-step kernel based optimization scheme (Mishra et al, 2009). Although this effective algorithm achieves accurate intra-retinal segmentation on rodent OCT images under low image contrast and in the presence of irregularly shaped structural features, results on images including the foveal pit region were not given and no quantitative evaluation using a large data set was provided.…”