Abstract
In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retinal features such as the thickness of the retina and the thickness of the individual retinal layers, and the volume of the pathologies such as drusen and hyper-reflective intra-retinal spots. We extract automatically, ten clinically important retinal features by segmenting individual SD-OCT images for classification purposes. The effectiveness of the extracted features is evaluated using several classification methods such as Random Forrest on 251 (59 normal, 177 AMD and 15 DME) subjects. We have performed 15-fold cross-validation tests for three phenotypes; DME, AMD and normal cases using these data sets and achieved accuracy of more than 95% on each data set with the classification method using Random Forrest. When we trained the system as a two-class problem of normal and eye with pathology, using the Random Forrest classifier, we obtained an accuracy of more than 96%. The area under the receiver operating characteristic curve (AUC) finds a value of 0.99 for each dataset. We have also shown the performance of four state-of-the-methods for classification the eye participants and found that our proposed method showed the best accuracy.
Highlights
Eye diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are amongst the most common causes of vision loss in our communities
We have reported the performance of Venhuizen et al [19], Lemaitre et al [2] and Sidibe et al [20] on D1 dataset from Sidibe et al An Area Under the receiver operator characteristics Curve (AUC) value was not reported by these researchers which is why the corresponding cells in Table 2 contain “NA”
We have proposed a novel method of eye disease classification using automatically quantified hand-crafted clinical driven features of AMD, DME and normal participants using the Random Forest algorithm
Summary
Eye diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are amongst the most common causes of vision loss in our communities. Different layers of the retina, the components of the choroid and the pathologies such as drusen and Hyper-reflective Intra-retinal Spots (HIS), are observable in the OCT image as shown in Fig 1(g) and 1(h) using the variation of the intensities and coherence due to reflective nature of the tissues and their thicknesses, Fig 1. There has been some work on the automatic segmentation of the retinal layers, but only a few methods are available for the classification of the SD-OCT volumes [13,14,15] Among those classification methods, most of them are binary classifier that is, classified into diseased or normal cases, not specific diseases such as AMD and DME. 2. Automatic feature extraction from the SD-OCT volumes that are related to the changes of the retinal structure due to AMD and DME (such as thickness of the retina and retinal layers, drusen)
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