Accurate segmentation of spectral-domain Optical Coherence Tomography (SD-OCT) images helps diagnose retinal pathologies and facilitates the study of their progression/remission. Manual segmentation is clinical-expertise dependent and highly time-consuming. Furthermore, poor image contrast due to high-reflectivity of some retinal layers and the presence of heavy speckle noise, pose severe challenges to the automated segmentation algorithms. The first step towards retinal OCT segmentation therefore, is to create a noise-free image with edge details still preserved, as achieved by image reconstruction on a wavelet-domain preceded by bilateral-filtering. In this context, the current study compares the effects of image denoising using a simple Gaussian-filter to that of wavelet-based denoising, in order to help investigators decide whether an advanced denoising technique is necessary for accurate graph-based intraretinal layer segmentation. A comparative statistical analysis conducted between the mean thicknesses of the six layers segmented by the algorithm and those reported in a previous study, reports non-significant differences for five of the layers (p > 0.05) except for one layer (p = 0.04), when denoised using Gaussian-filter. Non-significant layer thickness differences are seen between both the algorithms for all the six retinal layers (p > 0.05) when bilateral-filtering and wavelet-based denoising is implemented before boundary delineation. However, this minor improvement in accuracy is achieved at an expense of substantial increase in computation time (∼10s when run on a specific CPU) and logical complexity. Therefore, it is debatable if one should opt for advanced denoising techniques over a simple Gaussian-filter when implementing graph-based OCT segmentation algorithms.
Segmentation of spectral-domain Optical Coherence Tomography (SD-OCT) images facilitates visualization and
quantification of sub-retinal layers for diagnosis of retinal pathologies. However, manual segmentation is subjective,
expertise dependent, and time-consuming, which limits applicability of SD-OCT. Efforts are therefore being made to
implement active-contours, artificial intelligence, and graph-search to automatically segment retinal layers with accuracy
comparable to that of manual segmentation, to ease clinical decision-making. Although, low optical contrast, heavy
speckle noise, and pathologies pose challenges to automated segmentation. Graph-based image segmentation approach
stands out from the rest because of its ability to minimize the cost function while maximising the flow. This study has
developed and implemented a shortest-path based graph-search algorithm for automated intraretinal layer segmentation
of SD-OCT images. The algorithm estimates the minimal-weight path between two graph-nodes based on their gradients.
Boundary position indices (BPI) are computed from the transition between pixel intensities. The mean difference
between BPIs of two consecutive layers quantify individual layer thicknesses, which shows statistically insignificant
differences when compared to a previous study [for overall retina: p = 0.17, for individual layers: p > 0.05 (except one
layer: p = 0.04)]. These results substantiate the accurate delineation of seven intraretinal boundaries in SD-OCT images
by this algorithm, with a mean computation time of 0.93 seconds (64-bit Windows10, core i5, 8GB RAM). Besides
being self-reliant for denoising, the algorithm is further computationally optimized to restrict segmentation within the
user defined region-of-interest. The efficiency and reliability of this algorithm, even in noisy image conditions, makes it
clinically applicable.
Retinal layer shape and thickness are one of the main indicators in the diagnosis of ocular diseases. We present an active contour approach to localize intra-retinal boundaries of eight retinal layers from OCT images. The initial locations of the active contour curves are determined using a Viterbi dynamic programming method. The main energy function is a Chan-Vese active contour model without edges. A boundary term is added to the energy function using an adaptive weighting method to help curves converge to the retinal layer edges more precisely, after evolving of curves towards boundaries, in final iterations. A wavelet-based denoising method is used to remove speckle from OCT images while preserving important details and edges. The performance of the proposed method was tested on a set of healthy and diseased eye SD-OCT images. The experimental results, compared between the proposed method and the manual segmentation, which was determined by an optometrist, indicate that our method has obtained an average of 95.29%, 92.78%, 95.86%, 87.93%, 82.67%, and 90.25% respectively, for accuracy, sensitivity, specificity, precision, Jaccard Index, and Dice Similarity Coefficient over all segmented layers. These results justify the robustness of the proposed method in determining the location of different retinal layers.
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