Auto-detection of Optic Disc in Retinal Image using Transfer Learning Technique

Aim: To evaluate a new algorithm to detect the optic disc in retinal fundus images on a number of publicly available datasets. Optic Disc detection is an important first step in many automated algorithms, either to be masked out of future processing or for the use in optic disc related disease such as glaucoma and papilledema.Methods: We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image. The line segment with the maximum number of shortest paths is considered the optic disc location, with refinement from a combination template matching approach in the found region. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images. All images were acquired at a 45°-50° field of view.Results: The method achieves an accuracy of 100, 98.88, and 99.42% on the DRIVE, DIARETDB1, and Messidor databases respectively.Conclusions: The method performs as well or better than state-of-the-art methods on these datasets. Processing takes an average of 32 seconds (+-1.2) to detect the optic disc, with 26 of those seconds used for the vessel enhancement process. The accuracy over a wide variety of images shows that the method is robust and would be optimal for retinal analysis systems that perform vessel enhancement as part of their processing.

Retinal fundus images are commonly used by ophthalmologists to diagnose and monitor the development of eye diseases. One of the key information in the fundus images is to distinguish between the left and right eye. This work is motivated to provide an accurate and automated method to detect the left and right eye based on the retinal images.In current clinical practices, a common method to differentiate left and right eye in the retinal images is via the position of the optic nerve head with respect to the macula. This paper introduces a novel method to automatically detect the orientation of the retina in fundus images from the position of the central retinal artery and veins of the physiologic optic nerve head.Our method first locates the centre of the optic cup using pixel intensity. A 100 x 100 pixel ROI is then formed based on this centriod. Thresholding by pixel intensity and morphology is then used to segment the optic disc from this ROI to obtain an accurate optic disc and its blood vessels within. The pixel intensity in the green channel of this segmented disc along its horizontal axis is projected. The sum of the pixel intensity in the left half of the disc is then compared to the right half. The prevalence of higher green pixel intensity in either half of the optic disc would then indicate if this is a nasal or temporal sector, which would then indicate if this is a left or right eye.This method is tested on 194 images collected from patients at the Singapore Eye Research Institute and achieves an accuracy of 92.23%. This method is novel and its results are encouraging to provide an accurate and automatic system to make a distinction between left and right eye from retinal images.KeywordsAutomatedDetectionRetinalPixel Intensity

The optic disc (OD) is a critical anatomical pattern in retinal fundus images. The expertise to identify OD in fundus images play a significant part in automatic analysis frameworks. Any changes that occur in these anatomical structures result in retinal abnormalities. Hence it is essential to segment retinal features for detecting retinal diseases. Segmentation of the OD is significant for screening retinal disorder such as diabetic retinopathy(DR) and glaucoma. OD identification is a relatively easy task in standard retinal images, but Identifying this region in DR retinal images is difficult as Exudates-(EX) and OD appear the same features. In this paper, a novel technique using an operator splitting approach(OSA) for segmenting OD in fundus images with poor edges is proposed. A local database of 100 images collected from Silchar Medical College and Hospital(SMCH), Assam, and accuracy of 96% is obtained, which is better than other states of the art methods.

Optic disc (OD) segmentation is an essential step for automated detection of various serious ophthalmic pathologies related to diabetic retinopathy. Therefore, exudates detection is our major purpose, but we must extract the OD prior to the process because it appears with similar color, intensity and contrast to other characteristics of the retinal image. The retinal image consists of blood vessels that emerge from the OD. This paper presents a novel method for segmentation of the OD in retinal images. The methodology includes localization of OD center, followed by elimination of vascular structure using vesselness filter. Finally, an active contour model was applied to boundary OD segmentation. The results are compared with a ground truth image from the ophthalmologist. The source retinal image for performing this work is obtained from the publicly available DRIVE and MESSIDOR database. This method offers a successful segmentation of OD which may help in diagnosis of various retinal abnormalities.

In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its center is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use optic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then, we calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE, STARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success rate was 100, 91.36, and 98.9%, respectively.

The retinal optic disc is the region from where the central retinal artery and optical nerve of the retina emanate. Hence, it often serves as an important landmark and reference for other features in a retinal fundus image. The features obtained from a fundus images are often helpful in the diagnosis of various eye diseases. Locating and segmenting the optic disc are key pre-processing steps for extracting retinal features. This paper proposes a statistics-based method for locating a rectangular region of interest (ROI) containing the optic disc in a retinal fundus image. From a set of candidate rectangular regions, the method chooses the ROI using statistical features, namely the mean, standard deviation, and skewness of the pixel gray levels in the candidate regions. Since an optic disc is approximately round or slightly oval in the vertical direction, this study treats the maximal inscribed circle of the ROI as the initial contour of the optic disc and uses an active contour model (ACM) to precisely segment the optic disc further based on the initial contour. The experimental results show that the proposed statistics-based method combined with an ACM provides impressive performance in the segmentation of optic discs.

Glaucoma detection from retinal fundus images using graph convolution based multi-task model

Optic Disc (OD) localization is an important step in retinal image analysis especially for grading Diabetic retinopathy and Glaucoma. This paper describes a method to automatically localize the OD position in retinal fundus images. The method localizes the OD by combining intensity and vascular information. Initially, the vascular structure is extracted using 2 dimensional Gabor filtering followed by a simple thresholding technique. Then the initial OD candidates are selected using intensity information. The candidates’ satisfying area and density criterion are considered for the final stage. In the final stage, the entropy of vascular directions on the final candidates was calculated, and the candidate having maximum and optimum entropy was considered as OD region. The proposed method was evaluated on the two publicly available DRIVE and STARE databases. The method was able to obtain 100% of OD localization accuracy on DRIVE database with 2.2 seconds average computation time.

This study focuses on detecting and classifying neovascularization caused by proliferative diabetic retinopathy (PDR) in retinal fundus images. Image processing methods were applied to detect retinal vessels. A fractal analysis approach based on the box-counting method was used to quantify vascular patterns in normal and abnormal cases showing neovascularization near the optic disk (NVD). Ten images including five normal cases and five neovascularization cases were analyzed. The mean fractal dimension obtained for the NVD cases was 1.66 compared to the mean value of 1.52 for the normal cases. The statistical significance of the difference was high, with a p-value of 0.0088. The results show promise for use in detecting neovascularization in retinal images caused by PDR.

The aim of this survey is to list the various disease predictions from retinal fundus images and various methods used to detect the disease. This paper gives a detailed description about the various diseases predicted in retina by comparing retinal fundus image structure. Till now, the prediction of various diseases such as diabetic retinopathy, cardiovascular disease and other eye problems had been predicted by using retinal fundus images. Next, a comparitive study of the various methods followed using image processing to find out the diseases from retinal fundus images, is provided. The basic matrices observed to predict the diseases are optic disc,nerve cup and rim. To find the differences in the basic matrices, image processing techniques such as mask generation, colour normalization, edge detection, contrast enhancement are used. The datasets that are used for retinal image inputs are STARE, DRIVE, ONHSD, ARIA, IMAGERET. The survey at the end, discusses the future work for the possibilities of predicting gastreointestinal problems via retinal fundus images.

Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.

Optic disc detection and segmentation using saliency mask in retinal fundus images

Colour fundus images of the human retina are increasingly used in the diagnosis and treatment of several eye related pathologies and in ailments such as arteriosclerosis, diabetes, and hypertension. The effectiveness of treatment for many eye related diseases lies in the early detection through regular screenings. But, screening a large number of patients is a significant problem faced by medical practitioners in populous developing countries like India. In addition, there are extensive influences of human blunders and subjectivity on the consequences of assessment by a human master. This opens up the probability of applying propelled image processing techniques in fundus images to support and improve determination in different ways. Thus, automatic detection of pathologies and computer aided analysis in retinal images play a important role in modern diagnostic procedures and screening systems. Reliable and robust extraction of retinal features like optic disc, macula and vasculature is an essential for ensuing retinal image analysis and handling since these are the overwhelming and most stable structures showing up in the retina. However, automatic segmentation of retinal images is a complicated affair since retinal images are often poorly contrasted noisy, and there are a wide variations in orientation. This paper presents a review on algorithms or methods to detect optic disc automatically.

Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus Photographs

Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction.