The retinal nerve fiber layer (RNFL) is part of the retina located on the outside of the optic nerve head (ONH), which can be observed in a retinal fundus image. Visually, the ONH is a bright rounded or slightly oval area, and the RNFL is an area with distinctive texture that appears like a collection of striations with a whitish color. In normal eyes, the texture of the RNFL is clearly visible and distributed evenly in all sectors, while in glaucoma patients, the structure of the RNFL tends to be lost, which indicates defects of the RNFL [1].

A retinal fundus image is divided into four sectors, namely inferior (I), superior (S), nasal (N), and temporal (T). Sectors I and S are at the top and bottom, respectively. In the right eye, sectors N and T are on the right and left sides, and vice versa for the left eye. The thickest and most clearly visible parts of the RNFL structure are located in sectors I and S [2]. An overview of the retinal texture, which shows the differences between areas with and without (loss) RNFL and also the sector partition on retinal fundus image of the right eye, is shown in Figure 1.

The RNFL is a site of glaucomatous optic neuropathy, whose early changes need to be detected [1]. The detection of RNFL loss is necessary because glaucoma is an incurable disease that may cause permanent blindness. Glaucoma is the second most common cause of blindness in the world [3], and the number of glaucoma sufferers is expected to increase in 2020 [4]. Symptoms of glaucoma are generally not felt by the sufferer, and when glaucoma is finally detected, the damage to the structure of the retina may already be severe.

There are several digital imaging technologies that can be used to observe RNFL structures, namely optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy, scanning laser polarimetry, and fundus camera imaging [5]. In this research, we used images produced by fundus cameras to analyze the RNFL because they are more available in public healthcare and cheaper than other tools. In addition, fundus images can be utilized to diagnose and monitor other diseases, such as diabetes, hypertension, coronary heart disease, and stroke [6].

The RNFL can actually be easily detected by an ophthalmologist, but in certain cases, such as when the RNFL has an indistinct structure or for a large number of images, detection may be too time consuming and the result of RNFL detection may become subjective. Therefore, several approaches have been developed for computer-aided RNFL detection. To detect the RNFL, ONH segmentation is required because the area of the ONH does not contain the RNFL and must be eliminated. It is preceded by ONH localization, which is conducted to obtain a sub-image. The sub-image captures the whole ONH to simplify the subsequent process. This process is applied based on the green channel to predict the pixel candidates of the ONH [789]. Furthermore, in several previous works, segmentation of the ONH is implemented in the red channel because the ONH can be easily identified in this channel [9101112].

Works related to segmentation of the ONH are still scant and several previous studies are briefly summarized here. Two commonly used methods, namely threshold-based [91213] and active contour [781415], have been developed for ONH segmentation in the last 5 years. Further, fully automatic ONH segmentation by template matching was developed in, in which the superpixel method is applied by histograms and center surround statistics to determine the ONH and non-ONH area [16]. Moreover, an optimal framework by radon transformation of multi-overlapping windows was developed in [10]. In [17], the multiresolution sliding band filter (SBF) method is applied to predict the boundary of the ONH. In general, segmentation of the ONH yields a misclassification area that can be overcome using morphological filtering as in [791318].

Only few studies related to RNFL detection based on fundus image processing have been conducted. Progression based on fundus images has been performed in [1920] by comparing the results to the information in OCT images, while in [212223], the results are compared to the clinical data obtained from experts. A computer-aided method for analysis of the RNFL was proposed in [20] using texture analysis for prediction of the RNFL thickness with Gaussian Markov random fields (GMRF) and local binary patterns. Fundus images with blue and green layers from camera filtering by the standard blue-green filter BPB 45 provide the grayscale images used as experimental data in [19]. Texture analysis is also conducted in [22] by applying the fractal dimension method, while in [23] analysis of the RNFL is implemented by combining color and texture features. The color features are formed by two-color histograms computed in RGB and Lab color spaces. Meanwhile, the texture feature is obtained in the RGB space using local binary patterns. Furthermore, the quantification of the dark area around the ONH is performed in [21].

In this paper, we propose a method for automated detection of the RNFL based on the texture feature by forming a co-occurrence matrix derived from small areas (patches) outside the ONH where the RNFL is present, which are part of the retinal fundus image; this is the cornerstone of our contribution. Unlike the existing approach, which discards areas with blood vessels from the focal areas that need to be extracted, in this work, the extraction area may contain blood vessels. The area with blood vessels needs to be considered because the RNFL is easily detectable in this area. Another contribution is that we apply the backpropagation neural network (BPNN) method in classification to detect the presence of the RNFL on all patches, followed by discarding the isolated patches based on the neighbors in 8 directions to determine the presence of the RNFL in a sector.

The rest of this paper is organized as follows. The proposed method of RNFL detection, including its feature extraction and classification, are discussed in Section II. The datasets used in this research along with the performance of the proposed method based on our experiments are reported in Section III. Finally, Section IV discusses the results and concludes this paper.

We propose an automated RNFL detection method for application to retinal fundus images. This method is divided into two phases: the forming of features and testing, where each phase consists of several processes. In the feature forming stage, we obtain the feature values of several sample images used in the testing phase, while the testing phase aims to detect the RNFL in all sector. The framework of our proposed method is presented in Figure 2.

The performance of the proposed automated RNFL detection method was evaluated using a total of 40 retinal fundus images provided in JPEG format and obtained from glaucoma patients. The images were acquired using two types of different tools. First, 22 images were acquired by a 45° field of view (FOV) Topcon TRC-NW8 fundus camera and digitized at 4288 × 2848 pixels. Second, 18 images were acquired by a 30° FOV Carl Zeiss AG fundus camera incorporated into a Nikon N150 digital camera and digitized at 2240 × 1488 pixels. All the retinal fundus images were collected from Dr. Sardjito Hospital and Dr. YAP Eye Hospital, Yogyakarta with the approval of the Ethics Committee (No. IRB KE/FK/1108/EC/2015).

An ophthalmologist with 21 years of experience evaluated all of the retinal fundus images to provide the ground truth. The ground truth of each retinal fundus image was derived from 12 areas (sub-sectors) and 3 sub-sectors for each sector with an angle of 30° each. Therefore, there were 480 sub-sectors as the ground truth from the whole set of images used in this research. Labels ‘1’ or ‘0’ were given for each sub-sector, where ‘1’ indicated a sub-sector with RNFL, and ‘0’ indicated a sub-sector without RNFL (RNFL loss). The idea of forming 12 sub-sectors was adopted from the division of the area of OCT images based on ‘clock-hours’. This partitioning made the RNFL detection process by the expert more convenient and more detailed because the detection area becomes more focused. The RNFL detection of 12 sub-sectors needs to be considered because the occurrence of RNFL loss in almost all sub-sectors (especially in sectors I and S) indicates that glaucoma tends to be severe. The partition of sectors into sub-sectors and the labels provided by the expert for all the sub-sectors as the ground truth are shown in Figure 6. The order of the 12 sub-sectors are the following: sub-sectors 01 to 03 are in sector I, 04 to 06 are in sector S, 07 to 09 are in sector N, and 10 to 12 are in sector T.

The values of sensitivity, specificity, and accuracy are used to express the performance of our proposed method. Those value are computed as follows:

where true positive (TP) is the number of images detected as RNFL by the expert and the proposed method, true negative (TN) is the number of images detected as RNFL loss by the expert and the proposed method, false positive (FP) is the number of images detected as RNFL loss by the expert but detected as RNFL by the proposed method, and false negative (FN) is the number of images detected as RNFL by the expert but detected as RNFL loss by the proposed method. Those values lie between 0 to 1, and an accurate method should have values close to 1.

An example RNFL detection result for two fundus images performed by the ophthalmologist as ground truth (GT) and the proposed method (PM) in sub-sectors 01 to 12 is shown in Figure 7. Examples of erroneous false negative and false positive detections are shown in sub-sector 07 (Figure 7A) and sub-sector 11 (Figure 7B), respectively. Figure 7A shows a false negative RNFL detection result that occurred in sub-sector 07 because it is the boundary area between sectors T and S, where the RNFL texture tends to be indistinct. Furthermore, a false positive occurred in sub-sector 11, as shown in Figure 7B, because in that area, the texture is similar to mild RNFL.

The performance of the proposed method, expressed by the values of sensitivity, specificity, and accuracy, is reported in Table 1. Based on Table 1, the sub-sectors of sectors I and S show the highest accuracy of 100%, followed by sectors T and N with 95.45% and 93.18%, respectively. This matches our observation result based on the structure of the RNFL, namely that sectors I and S are the thickest and therefore have apparent texture. In sectors T and N, the sub-sectors mostly have the texture of mild RNFL, especially in the subsectors that are adjacent to sectors I and S, therefore accuracy decreases in both sectors.

The lowest value of accuracy in sectors N and T occurs in sub-sectors that are adjacent to the neighboring sectors of I or S. In this case, low accuracy occurs in sub-sectors 07 and 12 due to the gradual depletion of RNFL structures occurring in these sub-sectors. Sector T, especially in sub-sector 11, shows a high value of accuracy because total RNFL loss often occurs there. The result of RNFL detection is influenced by the RNFL structure in each sector as well as the accurate selection of RNFL or RNFL loss areas to form the sample images used for training data.

The performance of our proposed method using BPNN and other classifiers (k-NN and SVM) is reported in Table 2. Based on Table 2, it is clear that BPNN achieves optimal performance. The BPNN classifier achieves 92.31% accuracy with 95.44% sensitivity and 82.91% specificity. This result indicates that our proposed method, which uses two features selected with BPNN followed by discarding the isolated patch can be considered as a contribution in RNFL detection for its high level of accuracy.

Furthermore, we summarized several methods regarding the extraction of RNFL feature on the fundus images to give an insight into the development of the extraction feature for RNFL detection, which is reported in Table 3. Table 3 shows that there have only been a few studies related to this issue, and all of them have used their own local dataset to evaluate their method. The detection of RNFL by the proposed method achieves 94.52% accuracy. This surpasses the results of several previous works [212223], which achieved 93.8%, 78%, and 91.5% accuracy, respectively. Moreover, the approach proposed in [22] is limited to the extraction area by discarding the area of the blood vessels, and their sample images are obtained manually. This is different from our research, where we consider the blood vessels area as the potential pattern that is easily identifiable because the presence of RNFL overlaps some part of the blood vessels. The quantification of white pixels in [21] and rotation invariant using a local binary pattern in [23] have limitations for implementation in sector N and T. In those sectors, the texture of the RNFL tends to be too indistinct; therefore, the RNFL may be detected as the background. This issue can be overcome using features from GLCM by taking several sample images from all sub-sectors along with BPNN as a classifier; these method have been widely used to solve many cases of medical image classification.

An automated RNFL detection method by texture-based analysis has been proposed. The forty texture features are extracted using GLCM in RGB space. Those features are energy, contrast, correlation, homogeneity, entropy, autocorrelation, dissimilarity, cluster shade, cluster prominence, and maximum probability, which are extracted in four directions 0°, 45°, 90°, and 135°. Subsequently, feature selection by the CFS method is conducted to reduce the dimensions of the features. The feature selection step produces only two features, namely, autocorrelation 0° and correlation 90°. Then, we apply the BPNN method to classify the sub-sector based on the selected features. Our proposed method can distinguish which sub-sectors of a fundus image contain the RNFL or RNFL loss. The performance of our proposed method was evaluated using several retinal fundus images. A comparison with several previous methods, summarized in Table 3, shows that our method achieved a relatively high accuracy of 94.52%. Table 3 shows that the accuracy of our method surpasses the performance of previous methods [212223]. Moreover, our method only used two set of features that were smaller than those of the other methods; therefore, those features are implemented in our proposed method. The result demonstrate that our method successfully detects the part of the RNFL that overlaps the blood vessels. Nevertheless, the misclassification of the RNFL often occurs in sub-sectors 07, 09, 10, and 12 because our method is unable to handle the gradual depletion of RNFL structures occurring in these sub-sectors.

Several works related to the medical image classification have been conducted using deep learning, including [29] and [30], which used fundus image for different purposes. Deep learning has been used to detect red lesions in diabetic retinopathy evaluation and achieved an AUC of 0.93 using the MESSIDOR dataset in [29]. It has also been used to detect glaucoma with accuracy of 98.13% using 1,426 images in [30]. For future work, RNFL detection using the deep learning approach would be interesting to study since this method has shown great potential for extracting features and learning patterns from a large number of images.

In conclusion, we proposed a method of automated RNFL detection by texture-based analysis of the retinal fundus images using only two features (autocorrelation 0° and correlation 90°) and the BPNN classifier. The evaluation of our method showed that it achieved an accuracy of 94.52%. This result indicates that the two proposed texture features are suitable for RNFL detection. Our proposed method may support the ophthalmologist to diagnosis glaucoma based on retinal fundus images due to RNFL loss, indicating that the disease is becoming severe. It can also contribute to medical imaging analysis for glaucoma evaluation.