Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images

Abstract

This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD system is based on fusing novel OCT markers that describe both the morphology/anatomy and the reflectivity of retinal layers to improve DR diagnosis. This system separates retinal layers automatically using a segmentation approach based on an adaptive appearance and their prior shape information. High-order morphological and novel reflectivity markers are extracted from individual segmented layers. Namely, the morphological markers are layer thickness and tortuosity while the reflectivity markers are the 1st-order reflectivity of the layer in addition to local and global high-order reflectivity based on Markov-Gibbs random field (MGRF) and gray-level co-occurrence matrix (GLCM), respectively. The extracted image-derived markers are represented using cumulative distribution function (CDF) descriptors. The constructed CDFs are then described using their statistical measures, i.e., the 10th through 90th percentiles with a 10% increment. For individual layer classification, each extracted descriptor of a given layer is fed to a support vector machine (SVM) classifier with a linear kernel. The results of the four classifiers are then fused using a backpropagation neural network (BNN) to diagnose each retinal layer. For global subject diagnosis, classification outputs (probabilities) of the twelve layers are fused using another BNN to make the final diagnosis of the B-scan. This system is validated and tested on 130 patients, with two scans for both eyes (i.e. 260 OCT images), with a balanced number of normal and DR subjects using different validation metrics: 2-folds, 4-folds, 10-folds, and leave-one-subject-out (LOSO) cross-validation approaches. The performance of the proposed system was evaluated using sensitivity, specificity, F1-score, and accuracy metrics. The system's performance after the fusion of these different markers showed better performance compared with individual markers and other machine learning fusion methods. Namely, it achieved [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively, using the LOSO cross-validation technique. The reported results, based on the integration of morphology and reflectivity markers and by using state-of-the-art machine learning classifications, demonstrate the ability of the proposed system to diagnose the DR early.

Figure 1

Schematic illustration of the pipeline of the proposed system for DR diagnosis using OCT images.

Figure 2

Illustrative example of the proposed segmentation approach: (a) detection of vitreous and choroid, and (b) the final twelve segmented layers.

Figure 3

Sample example of the 12-layers segmentation for (a) a normal and (b) a DR case using our approach.

Figure 4

An illustrative example of the estimated CDF percentile feature of the 1st-order reflectivity for a normal and a DR case at the NPL layer.

Figure 5

OCT scan normalization: (a) original image, and (b) normalized image.

Figure 6

An illustrative color-coded example of the Gibbs energy for a normal (upper two rows) and a DR (lower two rows) case at three different layers: (a) NFL, (b) ONL, and (c) RPE layers.

Figure 7

Average and standard deviation of the extracted GLCM features: (a) homogeneity, (b) contrast, (c) correlation, and (d) energy for all normal and DR cases at the twelve layers.

Figure 8

An illustrative color-coded example of GLCM features for a normal (upper row) and a DR (lower row) case at three different layers: (a) NFL, (b) ONL, and (c) RPE layers.

Figure 9

Two examples of the estimated point-wise correspondences for the ONL layer for a normal (a) and a DR (b) case.

Figure 10

Color-coded examples of the estimated tortuosity for the ONL layer for (a) a normal and (b) a DR case.

Figure 11

Illustrative color-coded map examples of all retinal layers’ classification for a healthy retina (upper row) and a DR case (lower row) for individual markers: (a) Gibbs energy, (b) thickness, (c) tortuosity, (d) reflectivity, and (e) their fusion. The range of the color maps consists of 100 different colors for both normal (50 blue) and DR (50 red) classes to visually represent the probability of the classification from 0.5 to 1 with 0.1 increments.

Figure 12

Receiver operating characteristic (ROC) curves of the developed system in comparison with classification obtained using (a) individual markers; (b) different fusion approaches; and (c) different statistical ML. The area under the ROC curve (AUC) of the proposed system is $98.25\%$.