Statistics-Based Initial Contour Detection of Optic Disc on a Retinal Fundus Image Using Active Contour Model

The structure of the optic disc is impacted by the devastating eye disease glaucoma. Most doctors rely on viewing and screening the retinal fundus images to detect it. Identifying the optic disc is a crucial stage for automated diagnosis of glaucoma. This study presents a transfer learning-based approach to locate the optic disc in the retinal fundus image. The fundus images are categorized into four distinct classes according to the positions of optic disc to learn the in-depth specific features.Based on the YOLOv3 model, the optic disc detection method is then fine-tuned using the specialised image dataset. The model involved is trained and evaluated on 2178 retinal fundus images obtained from database at a nearby hospital and publically accessible datasets. In 2103 of the retinal fundus images, the suggested approach successfully detected the optical disc, yielding an average accuracy of 96.56%.

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

Automatic detection of optic disc and fovea is a precursor to the computer-aided analysis of retinal pathologies. In this paper, we present a unified approach for optic disc and fovea detection based on normalized cross-correlation technique. The algorithm performance is optimized by introducing vector inner products and norms instead of conventional mean and variance computations. We report optic disc detection results on four publicly available fundus image databases amounting to a total of 1451 fundus images and fovea detection results on another four publicly available fundus image databases amounting to a total of 1454 fundus images. The proposed method results in an optic disc detection accuracy of 99.01%, 95.67%, 99.09%, and 100% on DRISHTI-GS, MESSIDOR, DRIONS-DB, and DRIVE fundus image databases, respectively, and fovea detection accuracy of 94.83%, 84.62%, 95.51%, and 97.14% on MESSIDOR, DIARETDB0, DIARETDB1, and DRIVE fundus image databases, respectively. The speed of optic disc and fovea detection has been improved considerably by downsampling technique. In addition, we report the effect of downsampling on the detection accuracy.

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 detection and segmentation using saliency mask in retinal fundus images

Human eye is the most sophisticated organ, with perfectly interrelated subsystems such as retina, pupil, iris cornea, lens and optic nerve. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. Optic disc helps to identify the different stages of DR, and glaucoma. In this paper, a novel automated, reliable and efficient optic disc localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the optic disc (OD) due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy histon (A-IFSH) based segmentation to segment optic disc in retinal fundus images. Optic disc pixel intensity and column wise neighbourhood operation is employed to locate and isolate the optic disc. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous and 31 DR images. Our proposed method yielded precision-0.93, recall-0.91, F-score-0.92 and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with Otsu and Gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.

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.

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.

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.

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.

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

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.

In this research, retinal blood vessel fundus images will be segmented. Moreover, bifurcation point of the segmented blood vessel images will be determined. The data used for this research was obtained from fundus image database of DRIVE (Digital Retinal Images for Vessel Extraction) dataset. The preprocessing phases contain of several steps such as green channel extraction, histogram equalization and optic disc elimination. Meanwhile, the segmentation phase was performed by combining two filtering methods. These methods are median and derivative of Gaussian filter. Median filter was used to reduce the noise on retinal fundus image such as hard and soft exudates as well as dot and blot hemorrhages. Combination of these two filtering methods were also employed to determine the bifurcation point of segmented blood vessel image. The bifurcation points and the result from blood vessel segmentation were among two of the unique parameters of retinal fundus image. These parameters will be used eventually as unique features of the advanced research which is a biometric system to identify an individual based on his/her unique retinal pattern.

Optic Disc (OD) detection in retinal fundus images is a crucial stage for the automation of a screening system in diabetic ophthalmology. Most researches for automatic localization of OD benefit the regions of vessels. In this paper, we present a fast and novel method based on the Circlet Transform to detect OD in digital retinal fundus images that doesn't utilize the location of the vessels. First, each R, G and B band is enhanced using CLAHE method. Then, the enhanced image in RGB color space is converted to L*a*b one. Next, the Circlet transform is applied to the L* band, and finally, the Circlet transform coefficients are analyzed to find the location of the OD. The proposed algorithm is implemented on DRIVE dataset and the experimental results show a very well OD localization. The correct rate of the proposed method is 95% even though it doesn't utilize the vessels' structure.

Regular eye examinations are the key to limiting the vision loss caused by glaucoma and diabetic retinopathy. Optic disc (OD) detection is of vital importance in developing automated diagnosis systems for these diseases. In this work we present a method for automatic localization and boundary detection of the optic disc in retinal fundus images. In the first step, we rely on the OD geometric feature and utilize gaussian and mean curvatures for the localization of the OD. In the second step, we extract a region of interest based on the OD localization. Then, to the edges of this extracted region a circular Hough transform is applied to segment the OD boundary. The experimental results on three public datasets show the efficacy of the proposed method.