Automatic Optic Disc Segmentation Research Articles

Deep Convolutional Networks based on encoder-decoder architecture for automatic Optic Disc segmentation in retina images

Deep Convolutional Networks based on encoder-decoder architecture for automatic Optic Disc segmentation in retina images

A new optic disc segmentation method using a modified Dolph-Chebyshev matched filter

In this paper, a novel algorithm for automatic optic disc segmentation on fundus images is presented. In the proposed algorithm, a modified Dolph-Chebyshev type I function matched filter is utilized to detect optic disc boundary candidates. Subsequently, the Circular Hough Transform and B-spline approximation are employed to obtain the final optic disc boundary. The matched filter, Circular Hough Transform and B-spline approximation work on a localized region of interest that is obtained based on three independent methods: template-based, vessels density map-based, and maximum entropy-based methods. The performance of the proposed algorithm is evaluated using fundus images from the MESSIDOR and DRIVE datasets. From the experiments, it can be observed that the proposed algorithm successfully locates optic discs with an average success rate of above 99%. Also, the proposed algorithm can achieve best scores in terms of Area Overlap, Dices coefficient, Accuracy, and False Positive Fraction, which makes the algorithm suitable for practical applications such as an automatic detection tool for retinal diseases.

Automatic segmentation of optic disc and cup for CDR calculation

Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio (CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.

Automatic Optic Disc Segmentation Based on Modified Local Image Fitting Model with Shape Prior Information.

Accurate optic disc (OD) detection is an essential yet vital step for retinal disease diagnosis. In the paper, an approach for segmenting OD boundary without manpower named full-automatic double boundary extraction is designed. There are two main advantages in it. (1) Since the performances and the computational cost produced by iterations of contour evolution of active contour models- (ACM-) based approaches greatly depend on the initialization, this paper proposes an effective and adaptive initial level set contour extraction approach using saliency detection and threshold techniques. (2) In order to handle unreliable information generated by intensity in abnormal retinal images caused by diseases, a modified LIF approach is presented by incorporating the shape prior information into LIF. We test the effectiveness of the proposed approach on a publicly available DIARETDB0 database. Experimental results demonstrate that our approach outperforms well-known approaches in terms of the average overlapping ratio and accuracy rate.

Optic disc segmentation in fundus images using adversarial training

Glaucoma is one of the leading causes of blindness in the world. Optic disc segmentation is an indispensable step for automatic detection of glaucoma with fundus images. In this study, the authors propose an automatic optic disc segmentation approach using adversarial training. The improved ‘U-Net’ is used as the segmentation network to detect optic disc from fundus images, and then the authors add a ‘Patch-level’ adversarial network to enhance higher-order consistency between ground truth and the output from segmentation network, which further boosts the performance of segmentation network. In addition, a new loss function is designed to solve the problem of pixel-level class imbalance in small target region extraction of medical images. All these improvements have effectively increased the segmentation accuracy on hard examples. Authors’ methods achieve Dice coefficient of 0.967 on Drishti-GS dataset and 0.951 on RIM-ONEv3 dataset, which outperform most of the existing methods.

Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two‐Layer Level Set Method

Glaucoma is a group of eye conditions, which can seriously damage optic nerves because of an elevated intraocular pressure. Nowadays, glaucoma has become one of the principal causes of blindness that results in irreversible visual loss. Early screening and treatment of glaucoma can prevent further progression of optic nerve degeneration effectively. The vertical cup‐to‐disc ratio (CDR) is a commonly used measurement for the detection of glaucoma, and therefore accurate segmentation of optic disc (OD) and optic cup (OC) regions in retinal fundus images is of great significance. In this paper, we present a prior shape constraint‐based two‐layer level set method for OD and OC segmentation in fundus images. This method uses two different layers of one level set function to represent the OD and OC boundaries. In this method, the distance regularized term is designed to guarantee that the distance between the OD and OC varies smoothly. By introducing the prior shape constraint term energy, the final segmentation results of OD and OC are always in the shape of approximate ellipses. In addition, the proposed method has the property of dealing with the intensity inhomogeneity of fundus images through the local fitting energy embedded. Experiments on images from the Baidu Research database demonstrate that the proposed method has superior performance in terms of accuracy and effectiveness.

Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation.

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets.

NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure (IOP), which damages the vision of eyes. So, detecting and classifying Glaucoma is an important and demanding task in recent days. For this purpose, some of the clustering and segmentation techniques are proposed in the existing works. But, it has some drawbacks that include inefficient, inaccurate and estimates only the affected area. In order to solve these issues, a Neighboring Differential Clustering (NDC) — Intensity Variation Masking (IVM) are proposed in this paper. The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc. This work includes three stages such as, preprocessing, clustering and segmentation. At first, the given retinal image is preprocessed by using the Gaussian Mask Updated (GMU) model for eliminating the noise and improving the quality of the image. Then, the cluster is formed by extracting the threshold and patterns with the help of NDC technique. In the segmentation stage, the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method. Here, the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification. In experiments, the results of both existing and proposed techniques are evaluated in terms of sensitivity, specificity, accuracy, Hausdorff distance, Jaccard and dice metrics.

English

English

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.

Performance Evaluation of Optic Disc Segmentation Algorithms in Retinal Fundus Images : an Empirical Investigation

Careful evaluation of Optic nerve head structure and its documentation is extremely important for diagnosis of Glaucoma, an eye disease which leads to vision loss. This work focuses on automatic segmentation of Optic disc from fundus images, which is an important parameter for disease diagnosis. We investigate and compare performance of five methods used for Optic disc segmentation. These five methods are based on use of algorithms namely; distance regularized level set, Otsu thresholding, region growing, particle Swarm optimization, generalized regression neural network. For ease of comparison all the methods were implemented and tested on a single database. The method using generalized regression neural network best suits the said application. It outperforms the other four due to highest region agreement, lowest non overlap ratio, lowest relative absolute area difference and low execution time.

Automatic optic disc segmentation for analysis of the optic nerve head

We aimed to develop a deep learning (DL)–based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its thickness from the mapping and translating relations of spectral domain OCT (SD-OCT) thickness maps.Developing and evaluating an artificial intelligence detection tool.Pretraining paired data of color fundus photographs and SD-OCT images from 189 healthy participants and 371 patients with early glaucoma were used.The variational autoencoder (VAE) network training architecture was used for training, and the correlation between the fundus photographs and RNFL thickness distribution was determined through the deep neural network. The reference standard was defined as a vertical cup-to-disc ratio of ≥0.7, other typical changes in glaucomatous optic neuropathy, and RNFL defects. Convergence indicates that the VAE has learned a distribution that would enable us to produce corresponding synthetic OCT scans.Similarly to wide-field OCT scanning, the proposed model can extract the results of RNFL thickness analysis. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to assess signal strength and the similarity in the structure of the color fundus images converted to an RNFL thickness distribution model. The differences between the model-generated images and original images were quantified.We developed and validated a novel DL-based algorithm to extract thickness information from the color space of fundus images similarly to that from OCT images and to use this information to regenerate RNFL thickness distribution images. The generated thickness map was sufficient for clinical glaucoma detection, and the generated images were similar to ground truth (PSNR: 19.31 decibels; SSIM: 0.44). The inference results were similar to the OCT-generated original images in terms of the ability to predict RNFL thickness distribution.The proposed technique may aid clinicians in early glaucoma detection, especially when only color fundus photographs are available.