Optical Coherence tomography (OCT) images provide several indicators, e.g., the shape and the thickness of different retinal layers, which can be used for various clinical and non-clinical purposes. We propose an automated classification method to identify different ocular diseases, based on the local binary pattern features. The database consists of normal and diseased human eye SD-OCT images. We use a multiphase approach for building our classifier, including preprocessing, Meta learning, and active learning. Pre-processing is applied to the data to handle missing features from images and replace them with the mean or median of the corresponding feature. All the features are run through a Correlation-based Feature Subset Selection algorithm to detect the most informative features and omit the less informative ones. A Meta learning approach is applied to the data, in which a SVM and random forest are combined to obtain a more robust classifier. Active learning is also applied to strengthen our classifier around the decision boundary. The primary experimental results indicate that our method is able to differentiate between the normal and non-normal retina with an area under the ROC curve (AUC) of 98.6% and also to diagnose the three common retina-related diseases, i.e., Age-related Macular Degeneration, Diabetic Retinopathy, and Macular Hole, with an AUC of 100%, 95% and 83.8% respectively. These results indicate a better performance of the proposed method compared to most of the previous works in the literature.
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.
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.
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