Cup Region Research Articles

Multi-label deep learning for comprehensive optic nerve head segmentation through data of fundus images

Early diagnosis and continuous monitoring of patients with eye diseases are critical in computer-aided detection (CAD) techniques. Semantic segmentation, a key component in computer vision, enables pixel-level classification and provides detailed information about objects within images. In this study, we present three U-Net models designed for multi-class semantic segmentation, leveraging the U-Net architecture with transfer learning. To generate ground truth for the HRF dataset, we combine two U-Net models, namely MSU-Net and BU-Net, to predict probability maps for the optic disc and cup regions. Binary masks are then derived from these probability maps to extract the optic disc and cup regions from retinal images. The dataset used in this study includes pre-existing blood vessels and manually annotated peripapillary atrophy zones (alpha and beta) provided by expert ophthalmologists. This comprehensive dataset, integrating existing blood vessels and expert-marked peripapillary atrophy zones, fulfills the study's objectives. The effectiveness of the proposed approach is validated by training nine pre-trained models on the HRF dataset comprising 45 retinal images, successfully segmenting the optic disc, cup, blood vessels, and peripapillary atrophy zones (alpha and beta). The results demonstrate 87.7 % pixel accuracy, 87 % Intersection over Union (IoU), 86.9 % F1 Score, 85 % mean IoU (mIoU), and 15 % model loss, significantly contributing to the early diagnosis and monitoring of glaucoma and optic nerve disorders.

CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images.

Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. Nevertheless, limited approaches have been suggested to improve glaucoma classification due to issues like inadequate training data, variations in feature distribution, and the overall quality of samples. Furthermore, fundus images display significant similarities and slight discrepancies in lesion sizes, complicating glaucoma classification when utilizing ViTs. To address these obstacles, we introduce the contour-guided and augmented vision transformer (CA-ViT) for enhanced glaucoma classification using fundus images. We employ a Conditional Variational Generative Adversarial Network (CVGAN) to enhance and diversify the training dataset by incorporating conditional sample generation and reconstruction. Subsequently, a contour-guided approach is integrated to offer crucial insights into the disease, particularly concerning the optic disc and optic cup regions. Both the original images and extracted contours are given to the ViT backbone; then, feature alignment is performed with a weighted cross-entropy loss. Finally, in the inference phase, the ViT backbone, trained on the original fundus images and augmented data, is used for multi-class glaucoma categorization. By utilizing the Standardized Multi-Channel Dataset for Glaucoma (SMDG), which encompasses various datasets (e.g., EYEPACS, DRISHTI-GS, RIM-ONE, REFUGE), we conducted thorough testing. The results indicate that the proposed CA-ViT model significantly outperforms current methods, achieving a precision of 93.0%, a recall of 93.08%, an F1 score of 92.9%, and an accuracy of 93.0%. Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks.

A deep learning model based glaucoma detection using retinal images

The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. CONCLUSION: This article proposes the modified AlexNet deep learning models for the detections of Glaucoma utilizing retinal images. The OD region is detected using circulatory filter and OC region is detected using k-means classification algorithm. The detected OD and OC regions are utilized to classify the retinal images into either healthy or Glaucoma using the suggested AlexNet model. The proposed method obtains 100% Sey, 93.7% Spy and 96.6% CA on HRF dataset retinal images. The proposed AlexNet method obtains 97.7% Sey, 98% Spy and 97.8% CA on RIGA dataset retinal images. The proposed method stated in this article achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset.

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.

Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, AS-Net, Swin-Unet, and TransUNet).

Segmentation of Optic Disc and Optic Cup Based on Two-Layer Level Set with Sparse Shape Prior Constraint in Fundus Images

Glaucoma is a common high-incidence eye disease. The detection of the optic cup and optic disc in fundus images is one of the important steps in the clinical diagnosis of glaucoma. However, the fundus images are generally intensity inhomogeneity, and complex organizational structure, and are disturbed by blood vessels and lesions. In order to extract the optic disc and optic cup regions more accurately, we propose a segmentation method of the optic disc and optic cup in fundus image based on distance regularized two-layer level with sparse shape prior constraint. The experimental results show that our method can segment the optic disc and optic cup region more accurately and obtain satisfactory results.

Optic cup segmentation of stereo retinal fundus images using virtual reality

Glaucoma is one of the world leading causes of irreversible blindness. Early detection is essential to delay its progression and prevent vision loss. An accurate segmentation of the cup region in retinal fundus images is necessary to obtain relevant measurements for the detection of glaucoma. In recent years, multiple methods have been developed to automatically detect this region. All these methods are adjusted or trained using images that had been previously segmented by experts. In order to aid clinicians in performing this task, an interactive tool for the segmentation of the optic cup in stereo retinal fundus images using virtual reality has been developed. By using stereo images, the implemented virtual reality environment allows users to naturally perceive the three–dimensional structure of the optic cup region, which eases its segmentation compared to monocular images. The usage of the presented application was observed to increase accuracy of the delimitation, compared to using only two–dimensional fundus images, especially on areas with blood vessels.

Automated segmentation of optic disc and cup depicted on color fundus images using a distance-guided deep learning strategy

Optic disc (OD) and cup (OC) regions depicted on color fundus images are important landmarks for assessing glaucoma. In this study, we developed a novel and general distance-guided deep learning strategy (DGLS) to simultaneously segment the OD and OC from color fundus images based on an available network (i.e., U-Net). The developed method used two different types of annotation regions to characterize each target object and then converted the regions into location information leveraging a distance transform in a coarse-to-fine segmentation framework. We validated the developed algorithm by applying it to simultaneously segment OD and OC from color fundus images. Experiments on four public datasets (i.e., the REFUGE, BinRushed, DirshtiGS, and Magrabia datasets) suggested that the developed DGLS achieved, on average, a Dice Score (DS) of 0.9047, a Jaccard index (JI) of 0.8387, and a Hausdorff distance (HD, in pixel) of 2.6211 for the OD and OC in the coarse segmentation stage, and 0.9065, 0.8416, and 2.5930 in the fine segmentation stage. This algorithm demonstrated a superior performance compared to the U-Net and its several variants (i.e., Attention U-Net, BiO-Net and asymmetric network) trained on a single annotation and the traditional training strategy.

Effects of supportive and minimalist footwear on standing balance and walking stability in older women

BackgroundFootwear has been shown to influence balance and is an important consideration in relation to the prevention of falls. However, it remains unclear as to what type of footwear is most beneficial for balance in older people: sturdy, supportive footwear, or minimalist footwear to maximise plantar sensory input. The objectives of this study were therefore to compare standing balance and walking stability in older women wearing these two footwear styles, and to investigate participants’ perceptions in relation to comfort, ease of use and fit.MethodsOlder women (n = 20) aged 66 to 82 years (mean 73.4, SD 3.9) performed a series of laboratory tests of standing balance (eyes open and closed on floor and foam rubber mat, near tandem standing) and walking stability (treadmill, level and irregular surface) using a wearable sensor motion analysis system. Participants were tested wearing supportive footwear (incorporating design features to improve balance) and minimalist footwear. Perceptions of the footwear were documented using structured questionnaires.ResultsThere were no statistically significant differences in balance performance between the supportive and minimalist footwear. Participants perceived the supportive footwear to be significantly more attractive to self and others, easier to put on and off but heavier compared to the minimalist footwear. Overall comfort was similar between the footwear conditions, although the supportive footwear was reported to be significantly more comfortable in the heel, arch height, heel cup, heel width and forefoot width regions. Eighteen participants (90%) reported that they felt more stable in the supportive footwear and 17 (85%) reported that they would consider wearing them to reduce their risk of falling.ConclusionBalance performance and walking stability were similar in supportive footwear designed to reduce the risk of falling and minimalist footwear, although participants preferred the supportive footwear in relation to aesthetics, ease of use, comfort and perceived stability. Prospective studies are now required to ascertain the longer-term advantages and disadvantages of these footwear styles on comfort and stability in older people.Trial registrationAustralian New Zealand Clinical Trials Registry. ACTRN12622001257752p, 20/9/2022 (prospectively registered).

A novel approach to glaucoma screening using optic nerve head through image fusion and fractal geometry

The Glaucoma is a typical eye issue that causes vision loss. It leads to visual impairment if it is untreated on time. Normally, vision loss is slow and not perceptible. Regular and systematic eye assessments are suggested for persons from middle age to prevent further vision loss. The proposed system introduced a new technique in the field of ophthalmology to diagnose glaucoma in an effective way using Image Fusion and Fractal Geometry techniques. The optic cup and disc are extracted from fundus images using K-means and Thresholding techniques. Optic cups generated by the techniques are combined to obtain the better cup region using the image Fusion Technique to improve the glaucoma screening process. The same process is applied on optic discs to obtain the fused disc area. The Box counting fractal dimension estimation technique from Fractal Geometry is applied on fused areas to classify the image as either healthy or glaucoma. Results of these two techniques are evaluated on a publically available HRF dataset and obtained the accuracy of 97%.

A significant progress of computer aided diagnosis system using automated glaucoma screening

The automatic retinal image examination will be developing and significant screening device for initial recognition of eye diseases. This research proposes a computer aided diagnosis framework for initial recognition of glaucoma through Cup to Disc Ratio (CDR) measurement utilizing 2D fundus images. The system uses computer based analytical methods to procedure the patient data. The Glaucoma is chronic & progressive eye disease which damages optic nerve (ON) caused by improved intraocular pressure (IOP) of eye. The Glaucoma mostly affects on optic disc (OD) by enhancing cup size. It might lead to blindness whether not recognized initially. The glaucoma detection through “Heidelberg Retinal Tomography (HRT) optical Coherence Tomography (OCT)” have been more costly. This system proposes an efficient technique to recognize glaucoma utilizing 2D fundus image. The physical analysis of OD is a normal process utilized for identifying glaucoma. In this manuscript, we suggest automatic OD parameterization method is based on segmented OD and optic cup (OC) region attained from fundus retinal images. To automatically extract OD and optic cup, we used K-means clustering technique, SLIC (Simple linear iterative clustering) method, Gabor filter and thresholding. To reshape the attained disc and cup boundary ellipse fitting (EF) is applied to obtained image. We also propose a novel method which automatically calculates CDR from non-stereographic fundus camera images. The CDR is initial clinical indicator for glaucoma assessment. Also, we have calculated OD and cup area. These features are validated by classifying image either normal or glaucomatous for given patient.

Remote Screening for Optic Nerve Cupping Using Smartphone-based Nonmydriatic Fundus Photography.

Evaluation of nonmydriatic fundus photographs captured with a low-cost, smartphone-based camera facilitated remote screening of patients for enlarged optic nerve cup-to-disc ratio in the Independent Nation of Samoa, an underserved setting with one full-time ophthalmologist in the entire country. To investigate factors that impact inter-rater agreement of glaucoma suspect optic disc status using a low-cost, handheld nonmydriatic fundus camera. Color fundus photographs were obtained using the PanOptic iExaminer attached to an iPhone 6S by a lay examiner on 206 participants in the Independent Nation of Samoa. Images were remotely graded by an ophthalmologist and optometrist, and images from participants identified as at-risk for glaucoma were escalated to a glaucoma subspecialist for review. Fundus photo brightness, contrast, and focus were measured using the cup, rim, and temporal regions of the disc. Stereoscopic image pairs were subsequently generated from a subset of individual nonmydriatic photographs. Features suggestive of glaucoma based on optic disc cupping were identified in 16.0% (33/206) of participants. There was moderately strong agreement between graders (90.3%) with κ=0.53 [95% confidence interval (CI)=0.33-0.73]. The intraclass correlation coefficients for the cup-to-disc ratio (CDR) and its difference were 0.84 (95% CI=0.81-0.87) and 0.68 (95% CI=0.59-0.75). Of the 33 participants identified, 94% had clinical risk criteria for potential glaucoma when reviewed by a subspecialist. Color fundus photograph cup brightness was significantly associated with cup-to-disc (CDR) grade, R2=0.36 (P<0.001), in which a brighter disc yielded a higher CDR. Smartphone-based screening is a simple, low-cost method capable of measuring the CDR of the optic nerve. When combined with testing for other glaucoma risk factors such as intraocular pressure, this method of measuring CDR may help identify those patients who should be referred for further ophthalmologic assessment. We are currently conducting studies to assess the sensitivity and specificity of smart phone-based remote screening.

Automatic Glaucoma Detection from Stereo Fundus Images.

This paper proposes a new method for automatic detection of glaucoma from stereo pair of fundus images. The basis for detecting glaucoma is using the optic cup-to-disc area ratio, where the surface area of the optic cup is segmented from the disparity map estimated from the stereo fundus image pair. More specifically, we first estimate the disparity map from the stereo image pair. Then, the optic disc is segmented from one of the stereo image. Based upon the location of the optic disc, we perform an active contour segmentation on the disparity map to segment the optic cup. Thereafter, we can compute the optic cup-to-disc area ratio by dividing the area (i.e. the total number of pixels) of the segmented optic cup region to that of the segmented optic disc region. Our experimental results using the available test dataset shows the efficacy of our proposed approach.

Optic Cup segmentation from retinal fundus images using Glowworm Swarm Optimization for glaucoma detection

Glaucoma is one of the diseases that damages the optic nerve of the eye and can result in permanent vision loss. Hence, it becomes essential to detect the disorder at an early stage. Optic cup segmentation from retinal fundus images is an important step for automated glaucoma diagnosis. In this paper, we have presented Glowworm Swarm Optimization algorithm that helps in automated detection of optic cup from retinal fundus images. The glowworms as agents help in the construction of the solutions by making use of the intensity gradient inside the cup region. The exploration capability of glowworms is derived from the adaptive neighbourhood behaviour, thereby making them capable of detecting optic cup region accurately, even in images having weak cup boundaries or low contrast. The proposed algorithm has been implemented and evaluated on RIM-ONE, DRIVE, STARE, DRIONS-DB and DIARETDB1 datasets for qualitative and quantitative analysis. The average overlapping error obtained is 22.1% for DRIONS-DB Database which is minimum as compared to other approaches namely thresholding based, Ellipse fitting and Ant Colony optimization algorithm.

Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-Supervised Learning.

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays a significant role in clinical screening and early diagnosis of glaucoma. In general, obtaining CDR is subjected to measuring on manually or automatically segmented optic disc and cup. Despite great efforts have been devoted, obtaining CDR values automatically with high accuracy and robustness is still a great challenge due to the heavy overlap between optic cup and neuroretinal rim regions. In this paper, a direct CDR estimation method is proposed based on the well-designed semi-supervised learning scheme, in which CDR estimation is formulated as a general regression problem while optic disc/cup segmentation is cancelled. The method directly regresses CDR value based on the feature representation of optic nerve head via deep learning technique while bypassing intermediate segmentation. The scheme is a two-stage cascaded approach comprised of two phases: unsupervised feature representation of fundus image with a convolutional neural networks (MFPPNet) and CDR value regression by random forest regressor. The proposed scheme is validated on the challenging glaucoma dataset Direct-CSU and public ORIGA, and the experimental results demonstrate that our method can achieve a lower average CDR error of 0.0563 and a higher correlation of around 0.726 with measurement before manual segmentation of optic disc/cup by human experts. Our estimated CDR values are also tested for glaucoma screening, which achieves the areas under curve of 0.905 on dataset of 421 fundus images. The experiments show that the proposed method is capable of state-of-the-art CDR estimation and satisfactory glaucoma screening with calculated CDR value.

A novel equalization scheme for the selective enhancement of optical disc and cup regions and background suppression in fundus imagery

The ratio of the diameters of Optic Cup (OC) and Optic Disc (OD), termed as ‘Cup to Disc Ratio’ (CDR), derived from the fundus imagery is a popular biomarker used for the diagnosis of glaucoma. Demarcation of OC and OD either manually or through automated image processing algorithms is error prone because of poor grey level contrast and their vague boundaries. A dedicated equalization which simultaneously compresses the dynamic range of the background and stretches the range of ODis proposed in this paper. Unlike the conventional GHE, in the proposed equalization, the original histogram is inverted and weighted nonlinearly before computing the Cumulative Probability Density (CPD). The equalization scheme is compared with Adaptive Histogram Equalization (AHE), Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in terms of the difference between the mean grey levels of OD and the background, using a quantitative metric known as Contrast Improvement Index (CII). The CII exhibited by CLAHE, GHE and the proposed scheme are 1.1977 ± 0.0326, 1.0862 ± 0.0304 and 1.3312 ± 0.0486, respectively.The proposed method is observed to be superior to CLAHE, GHE and AHE and it can be employed in Computerized Clinical Decision Support Systems (CCDSS) to improve the accuracy of localizing the OD and the computation of CDR.

An automated glaucoma screening system using cup-to-disc ratio via Simple Linear Iterative Clustering superpixel approach

Glaucoma is an ocular disease caused by damaged optic nerve head (ONH) due to high intraocular pressure (IOP) within the eyeball. Usually, glaucoma patients will not realize the presence of this disease due to lack of visible early symptoms such as pain and redness mark. The disease can cause permanent blindness if it is not treated immediately. Hence, glaucoma screening is very crucial in detecting the disease during the early stages. There are various types of glaucoma screening tests such as tonometry test which is based on IOP measurement, ophthalmology test which is based on shape and color of the eyes, and pachymetry test which is based on complete field vision measurement. All these three screening tests involve manual assessment which is time-consuming and costly. Therefore, an efficient glaucoma screening system that can automatically analyze the severity level of the disease is very much needed. Thus, the main objective of this paper is to develop an automatic glaucoma screening system based on superpixel classification by providing a high-quality input image. Firstly, input images are undergone preprocessing methods to cater for noise removal and illumination correction. This is emphasized in the implementation of the anisotropic diffusion filter and illumination correction method. The pixels of the input images are then aggregate into superpixels using Simple Linear Iterative Clustering (SLIC) approach. Then, image features based on histogram data and textural information are extracted on each superpixel using statistical pixel-level (SPL) method. The prominent features are then fed into Support Vector Machine (SVM) classifier to classify each superpixel into optic disc, optic cup, blood vessel, and background regions. The classifier is also used to determine the boundaries of both optic disc and optic cup. Lastly, the segmented optic disc and optic cup are used to determine the presence of glaucoma using cup-to-disc ratio (CDR) measurement. The proposed method has been tested on RIM-One database. The experimental results have successfully distinguished optic disc and optic cup from the background with an average accuracy and sensitivity of 98.6% and 92.3%, respectively tested on linear kernel.

JointRCNN: A Region-Based Convolutional Neural Network for Optic Disc and Cup Segmentation.

The purpose of this paper is to propose a novel algorithm for joint optic disc and cup segmentation, which aids the glaucoma detection. By assuming the shapes of cup and disc regions to be elliptical, we proposed an end-to-end region-based convolutional neural network for joint optic disc and cup segmentation (referred to as JointRCNN). Atrous convolution is introduced to boost the performance of feature extraction module. In JointRCNN, disc proposal network (DPN) and cup proposal network (CPN) are proposed to generate bounding box proposals for the optic disc and cup, respectively. Given the prior knowledge that the optic cup is located in the optic disc, disc attention module is proposed to connect DPN and CPN, where a suitable bounding box of the optic disc is first selected and then continued to be propagated forward as the basis for optic cup detection in our proposed network. After obtaining the disc and cup regions, which are the inscribed ellipses of the corresponding detected bounding boxes, the vertical cup-to-disc ratio is computed and used as an indicator for glaucoma detection. Comprehensive experiments clearly show that our JointRCNN model outperforms state-of-the-art methods for optic disc and cup segmentation task and glaucoma detection task. Joint optic disc and cup segmentation, which utilizes the connection between optic disc and cup, could improve the performance of optic disc and cup segmentation. The proposed method improves the accuracy of glaucoma detection. It is promising to be used for glaucoma screening.

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central region which is the optic disc and the optic cup region within the disc are examined to determine one of the important cues for glaucoma diagnosis called the optic cup-to-disc ratio (CDR). CDR calculation requires accurate segmentation of optic disc and cup. Another important cue for glaucoma progression is the variation of depth in ONH region. In this paper, we first propose a deep learning framework to estimate depth from a single fundus image. For the case of monocular retinal depth estimation, we are also plagued by the labeled data insufficiency. To overcome this problem we adopt the technique of pretraining the deep network where, instead of using a denoising autoencoder, we propose a new pretraining scheme called pseudo-depth reconstruction, which serves as a proxy task for retinal depth estimation. Empirically, we show pseudo-depth reconstruction to be a better proxy task than denoising. Our results outperform the existing techniques for depth estimation on the INSPIRE dataset. To extend the use of depth map for optic disc and cup segmentation, we propose a novel fully convolutional guided network, where, along with the color fundus image the network uses the depth map as a guide. We propose a convolutional block called multimodal feature extraction block to extract and fuse the features of the color image and the guide image. We extensively evaluate the proposed segmentation scheme on three datasets- ORIGA, RIMONEr3, and DRISHTI-GS. The performance of the method is comparable and in many cases, outperforms the most recent state of the art.

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.

Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection.

Glaucoma is one of the major causes of blindness. Researchers keep looking for better ways to detect glaucoma in its early stage before it gets worse and cannot be cured. Among existing methods, the vertical cup to disc ratio (CDR) has been found to be effective for glaucoma measurement, which is calculated from the diameters of the optic cup and disc regions. Therefore, in order to achieve a more accurate CDR, a good segmentation of the optic disc and cup regions is quite important. Noting that the shape of the disc and cup regions can be assumed to be an ellipse, in this work we propose to find the minimal bounding boxes for the two regions based on the recent advances of deep learning. Also, considering blood vessels, passing through the disc area in a fundus image, can affect the detection of the bounding boxes, we further propose to remove the blood vessels beforehand in order to further boost the overall performance. Comprehensive experiments show that our proposed method achieves state-of-the-art performance on ORIGA-650 for optic disc and cup segmentation.