DETECTION AND SEGMENTATION OF OPTIC DISC IN FUNDUS IMAGES

Objective: Image processing technique is utilized in the medical field widely nowadays. Hence, therefore, this technique is used to extract the different features like blood vessels, optic disk, macula, fovea etc. automatically of the retinal image of eye.Methods: This paper presents a simple and fast algorithm using Mathematical Morphology to find the fovea of fundus retinal image. The image for analysis is obtained from the DRIVE database. Also, this paper is enhanced to detect the Diabetic Retinopathy disease occurring in the eye.Results: Detection of optic disc boundary becomes important for the diagnosis of glaucoma. The iterative curve evolution was stopped at the image boundaries where the energy was minimum.Conclusion: The changes in the shape and size of the optic disc can be used to detect glaucoma and also cup ratio can be used as a measure of glaucoma

Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio( CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN) . A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images

Optic Disc and Macula Localization from Retinal Optical Coherence Tomography and Fundus Image

This research used images from Optical Coherence Tomography (OCT) examination as well as fundus images to localize the optical disc and macular layer of retina. The researchers utilized the OCT and fundus image to interpret the distance between macular center and optic disc in the image. The distance will express the area of macula that can be employed for further research. This distance could recognize the thickness of macula parameters diameter that will be used in localizing process of optic disc and macula. The parameters are the circle radius, the size of window’s filter, the constant value and the size of optic disc element structure as well as the size of macula. The results of this study are expected to improve the accuracy of macula detection that experience the edema

Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region

Optical Coherence Tomography Interpretation for Glaucoma

Structural glaucomatous changes occur more frequently in the earlier stages of glaucoma than functional defects so we should give special care on oct (optical coherence tomography) importance as the best current method. RNFL change detection are more useful in early glaucoma, GCC in moderate to advanced glaucoma while visual field test is more useful in advanced stages but overall using a combination of RNFL, ONH and macular measurement modalities is recommended for glaucoma evaluation because each of these parameters may be affected earlier than the others so, taking into account the findings from the RNFL, ONH and macula enhances early diagnosis of glaucoma

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75

Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach

Optic disc (OD) is a key structure in retinal images. It serves as an indicator to detect various diseases such as glaucoma and changes related to new vessel formation on the OD in diabetic retinopathy (DR) or retinal vein occlusion. OD is also essential to locate structures such as the macula and the main vascular arcade. Most existing methods for OD localization are rule-based, either exploiting the OD appearance properties or the spatial relationship between the OD and the main vascular arcade. The detection of OD abnormalities has been performed through the detection of lesions such as hemorrhaeges or through measuring cup to disc ratio. Thus these methods result in complex and inflexible image analysis algorithms limiting their applicability to large image sets obtained either in epidemiological studies or in screening for retinal or optic nerve diseases. In this paper, we propose an end-to-end supervised model for OD abnormality detection. The most informative features of the OD are learned directly from retinal images and are adapted to the dataset at hand. Our experimental results validated the effectiveness of this current approach and showed its potential application