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Objective: Many common eye diseases and cardiovascular diseases can be diagnosed through retinal imaging. However, due to uneven illumination, image blurring, and low contrast, retinal images with poor quality are not useful for diagnosis, especially in automated image analyzing systems. Here, we propose a new image enhancement method to improve color retinal image luminosity and contrast. Methods: A luminance gain matrix, which is obtained by gamma correction of the value channel in the HSV (hue, saturation, and value) color space, is used to enhance the R, G, and B (red, green and blue) channels, respectively. Contrast is then enhanced in the luminosity channel of L*a*b* color space by CLAHE (contrast-limited adaptive histogram equalization). Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images. Results: The performance of the method is mainly validated on a dataset of 961 poor-quality retinal images. Quality assessment (range 0–1) of image enhancement of this poor dataset indicated that our method improved color retinal image quality from an average of 0.0404 (standard deviation 0.0291) up to an average of 0.4565 (standard deviation 0.1000). Conclusion: The proposed method is shown to achieve superior image enhancement compared to contrast enhancement in other color spaces or by other related methods, while simultaneously preserving image naturalness. Significance: This method of color retinal image enhancement may be employed to assist ophthalmologists in more efficient screening of retinal diseases and in development of improved automated image analysis for clinical diagnosis. [ABSTRACT FROM PUBLISHER]

Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed without any hardware modifications. Additionally, we propose a unified multi-scale reconstruction framework, namely Multiscale-Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled (compressed) OCT images with flexible magnification factors. We incorporate the proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary samples with clinical features such as stent and calcified lesions. Our experimental results demonstrate that spectral-spatial down-scaled data can be better reconstructed than data that are down-scaled solely in either spectral or spatial domain. Moreover, we observe better reconstruction performance using MSSMN than using existing reconstruction methods. Our acquisition method and multi-scale reconstruction framework, in combination, may allow faster SD-OCT inspection with high resolution during coronary intervention. [ABSTRACT FROM AUTHOR]

Objective: Presbyopia, an age-related ocular disorder, is characterized by the loss in the accommodative abilities of the human eye. Conventional methods of correcting presbyopia divide the field of view, thereby resulting in significant vision impairment. We demonstrate the design, assembly and evaluation of autofocusing eyeglasses for restoration of accommodation without dividing the field of view. Methods: The adaptive optics eyeglasses comprise of two variable-focus liquid lenses, a time-of-flight range sensor and low-power, dual microprocessor control electronics, housed within an ergonomic frame. Subject-specific accommodation deficiency models were utilized to demonstrate high-fidelity accommodative correction. The abilities of this system to reduce accommodation deficiency, its power consumption, response time, optical performance and MTF were evaluated. Results: Average corrected accommodation deficiencies for 5 subjects ranged from -0.021 D to 0.016 D. Each accommodation correction calculation was performed in ∼67 ms which consumed 4.86 mJ of energy. The optical resolution of the system was 10.5 cycles/degree, and featured a restorative accommodative range of 4.3 D. This system was capable of running for up to 19 hours between charge cycles and weighed ∼132 g. Conclusion: The design, assembly and performance of an autofocusing eyeglasses system to restore accommodation in presbyopes has been demonstrated. Significance: The new autofocusing eyeglasses system presented in this article has the potential to restore pre-presbyopic levels of accommodation in subjects diagnosed with presbyopia. [ABSTRACT FROM AUTHOR]

Goal: The full-field electroretinogram (ffERG) is an objective tool to assess global retinal function, though as it is currently done, it is unable to localize sources of retinal dysfunction or damage. To overcome this, we have developed a new way to record multiple spatial derivations of the ERG using the rotating capability of the eye, thus creating “virtual electrodes”. We have termed this the multi-angular ERG (or maERG). With only 3 real electrodes and 11 varying gaze positions, we create 33 “virtual electrodes”. Methods: We created a realistic electrophysiological and anatomical eye model (i.e., forward model) to reconstruct the retinal activity (i.e., inverse problem) from the 33 virtual electrodes. We simulated 2 pathological scenarios (central and peripheral scotomas), which were compared to their respective theoretical source configurations using an Area under Receiver Operator Characteristic curve metric. Results: Our simulations show that the low-resolution brain electromagnetic tomography algorithm (LORETA) is the best method tested to reconstruct retinal sources when compared to the Minimum Norm Estimate algorithm. Furthermore, a signal to noise ratio of 50 dB is needed to accurately reconstruct the retina's functional map. Conclusion: Our proposed maERG recording method, combined with our solution to the electromagnetic inverse problem enables us to reconstruct the functional map of the human retina. Significance: We believe that this new functional retinal imaging technique will permit earlier detection of retinal malfunction and consequently optimize the clinical monitoring of patients affected with retinopathies. [ABSTRACT FROM AUTHOR]

Objective: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ratios from fundus and optical coherence tomography scans. However, this paper presents a novel strategy that pays attention to the RGC atrophy for screening glaucomatous pathologies and grading their severity. Methods: The proposed framework encompasses a hybrid convolutional network that extracts the retinal nerve fiber layer, ganglion cell with the inner plexiform layer and ganglion cell complex regions, allowing thus a quantitative screening of glaucomatous subjects. Furthermore, the severity of glaucoma in screened cases is objectively graded by analyzing the thickness of these regions. Results: The proposed framework is rigorously tested on publicly available Armed Forces Institute of Ophthalmology (AFIO) dataset, where it achieved the F1 score of 0.9577 for diagnosing glaucoma, a mean dice coefficient score of 0.8697 for extracting the RGC regions and an accuracy of 0.9117 for grading glaucomatous progression. Furthermore, the performance of the proposed framework is clinically verified with the markings of four expert ophthalmologists, achieving a statistically significant Pearson correlation coefficient of 0.9236. Conclusion: An automated assessment of RGC degeneration yields better glaucomatous screening and grading as compared to the state-of-the-art solutions. Significance: An RGC-aware system not only screens glaucoma but can also grade its severity and here we present an end-to-end solution that is thoroughly evaluated on a standardized dataset and is clinically validated for analyzing glaucomatous pathologies. [ABSTRACT FROM AUTHOR]

Diabetic Macular Edema (DME) and macular edema secondary to retinal occlusion (RVO) are the two most common retinal vascular causes of visual impairment and leading cause of worldwide vision loss. The blood-retinal barrier is the key barrier for maintaining fluid balance within the retinal tissue. Vascular Endothelial Growth Factor (VEGF) has a significant role in the permeability of the blood-retinal barrier, which also leads to appearance of leakage foci. Intravitreal anti-VEGF therapy is the current gold standard treatment and has been demonstrated to improve macular thickening, improve vision acuity and reduce vascular leakage. However, treatment response and required dosing interval can vary widely across patients. Given the role of the blood-retinal barrier and vascular leakage in the pathogenesis of these disorders, the goal of this study was to present and evaluate new computer extracted features relating to morphology, spatial architecture and tortuosity of vessels and leakages from baseline ultra-widefield fluorescein angiography (UWFA) images. Specifically, we sought to evaluate the role of these computer extracted features from baseline UWFA images. Notably, these UWFA images were obtained from IRB-approved PERMEATE clinical trial , to distinguish eyes tolerating extended dosing intervals (n = 16) who are referred to as non-rebounders and those who require more frequent dosing (n = 12) and are called rebounders based on visual acuity loss with extended dosing challenges. A total of 64 features encapsulating different morphological and geometrical attributes of leakage patches including the anatomical (shape, size, density, area, minor and major axis, orientation, area, extent ratio, perimeter, radii) and geometrical characteristics (the proximity of each leakage foci to main vessels, to other leakage foci and to optical disc) as well as 54 tortuosity features (tortuosity of whole vessel network, local tortuosity of vessels in the vicinity of leakage foci) were extracted. The most significant and predictive biomarkers related to treatment response were proximity of leakage nodes to major and minor eye vessels as well as local vasculature tortuosity in the vicinity of the leakages. The imaging features were then used in conjunction with a Linear Discriminant Analysis (LDA) classifier to distinguish rebounders from non-rebounders. The 3-fold cross-validated Area Under Curve (AUC) was found to be 0.82 for the morphological based features and 0.85 for the tortuosity based features. Our findings suggest higher variation in leakage node proximity to retinal vessels in eyes tolerating extended interval dosing. In contrast, eyes with increased local vascular tortuosity demonstrated less tolerance of increased dosing interval. Moreover, a class activation map generated by a deep learning model identified regions that corresponded to regions of leakages proximal to the vessels, providing confirmation of the validity of predictive image features extracted from these regions in this study. [ABSTRACT FROM AUTHOR]

Objective: The focal length of available optical see-through (OST) head-mounted displays (HMDs) is at least 2 m; therefore, during manual tasks, the user eye cannot keep in focus both the virtual and real content at the same time. Another perceptual limitation is related to the vergence-accommodation conflict, the latter being present in binocular vision only. This paper investigates the effect of incorrect focus cues on the user performance, visual comfort, and workload during the execution of augmented reality (AR)-guided manual task with one of the most advanced OST HMD, the Microsoft HoloLens. Methods: An experimental study was designed to investigate the performance of 20 subjects in a connect-the-dots task, with and without the use of AR. The following tests were planned: AR-guided monocular and binocular, and naked-eye monocular and binocular. Each trial was analyzed to evaluate the accuracy in connecting dots. NASA Task Load Index and Likert questionnaires were used to assess the workload and the visual comfort. Results: No statistically significant differences were found in the workload, and in the perceived comfort between the AR-guided binocular and monocular test. User performances were significantly better during the naked eye tests. No statistically significant differences in performances were found in the monocular and binocular tests. The maximum error in AR tests was 5.9 mm. Conclusion: Even if there is a growing interest in using commercial OST HMD, for guiding high-precision manual tasks, attention should be paid to the limitations of the available technology not designed for the peripersonal space. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this paper is to propose a novel algorithm for joint optic disc and cup segmentation, which aids the glaucoma detection. Methods: 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. Results: 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. Conclusion: 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. Significance: The proposed method improves the accuracy of glaucoma detection. It is promising to be used for glaucoma screening. [ABSTRACT FROM AUTHOR]

In both movement training and neurorehabilitation, there have been numerous examples of how average performance can be manipulated through practice using enhanced visual feedback. Objective: Rather than just influencing the mean, our objective was to use a novel feedback technique called limit-push to influence the trial-to-trial variability of motion by distorting vision. Method : Limit-push was previously done using robotic forces; the present study employed only visual distortions that imitated the limit-push approach. Results: Like the robotic force treatment, our results showed how subjects significantly shifted the distributions of their motions. This effect was even greater than that of the original limit-push experiment that used robotic forces. Significance : Such visual distortion interventions do not require a robot for enhanced training. Conclusion: The visual limit-push technique appears to be able to selectively alter both the central tendency and variability in performance training applications. [ABSTRACT FROM AUTHOR]

Objective: Deep learning based methods for retinal vessel segmentation are usually trained based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to-pixel matching between a predicted probability map and the corresponding manually annotated segmentation. However, due to the highly imbalanced pixel ratio between thick and thin vessels in fundus images, a pixel-wise loss would limit deep learning models to learn features for accurate segmentation of thin vessels, which is an important task for clinical diagnosis of eye-related diseases. Methods: In this paper, we propose a new segment-level loss which emphasizes more on the thickness consistency of thin vessels in the training process. By jointly adopting both the segment-level and the pixel-wise losses, the importance between thick and thin vessels in the loss calculation would be more balanced. As a result, more effective features can be learned for vessel segmentation without increasing the overall model complexity. Results: Experimental results on public data sets demonstrate that the model trained by the joint losses outperforms the current state-of-the-art methods in both separate-training and cross-training evaluations. Conclusion: Compared to the pixel-wise loss, utilizing the proposed joint-loss framework is able to learn more distinguishable features for vessel segmentation. In addition, the segment-level loss can bring consistent performance improvement for both deep and shallow network architectures. Significance: The findings from this study of using joint losses can be applied to other deep learning models for performance improvement without significantly changing the network architectures. [ABSTRACT FROM AUTHOR]

People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and intervisit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of \text80\% at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we propose a robust, efficient, and automatic reconnection algorithm for bridging interrupted curvilinear skeletons in ophthalmologic images. Methods: This method employs the contour completion process, i.e., mathematical modeling of the direction process in the roto-translation group SE(2) \equiv \mathbb R^2 \rtimes S^1 to achieve line propagation/completion. The completion process can be used to reconstruct interrupted curves by considering their local consistency. An explicit scheme with finite-difference approximation is used to construct the three-dimensional (3-D) completion kernel, where we choose the Gamma distribution for time integration. To process structures in $SE(2)$, the orientation score framework is exploited to lift the 2-D curvilinear segments into the 3-D space. The propagation and reconnection of interrupted segments are achieved by convolving the completion kernel with orientation scores via iterative group convolutions. To overcome the problem of incorrect skeletonization of 2-D structures at junctions, a 3-D segment-wise thinning technique is proposed to process each segment separately in orientation scores. Results: Validations on 4 datasets with different image modalities show that our method achieves an average success rate of $95.24\%$ in reconnecting $40\,457$ gaps of sizes from $7 \times 7$ to $39 \times 39$, including challenging junction structures. Conclusion: The reconnection approach can be a useful and reliable technique for bridging complex curvilinear interruptions. Significance: The presented method is a critical work to obtain more complete curvilinear structures in ophthalmologic images. It provides better topological and geometric connectivities for further analysis. [ABSTRACT FROM PUBLISHER]

This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image $g$ is transformed into three sets: $T$ (true), $I$ (indeterminate) that represents noise, and $F$ (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set $I$ , and a new $\lambda$ -correction operation is introduced to compute the set $T$ in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively. [ABSTRACT FROM PUBLISHER]

Objective: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with the appearance of different types of lesions that include microaneurysms, hemorrhages, exudates, etc. Detection of these lesions plays a significant role for early diagnosis of DR. Methods: To this aim, this paper proposes a novel and automated lesion detection scheme, which consists of the four main steps: vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing. The optic disc and the blood vessels are suppressed first to facilitate further processing. Curvelet-based edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal background, while the contrast between the bright lesions and the background is enhanced through an optimally designed wideband bandpass filter. The mutual information of the maximum matched filter response and the maximum Laplacian of Gaussian response are then jointly maximized. Differential evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions that determine the thresholds of segmenting the candidate regions. Morphology-based postprocessing is finally applied to exclude the falsely detected candidate pixels. Results and Conclusions: Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of $97.71\%$ and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties. [ABSTRACT FROM PUBLISHER]

Objective: Accurate segmentation of neurosensory retinal detachment (NRD) associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) is vital for the assessment of central serous chorioretinopathy (CSC). A novel two-stage segmentation algorithm was proposed, guided by Enface fundus imaging. Methods: In the first stage, Enface fundus image was segmented using thickness map prior to detecting the fluid-associated abnormalities with diffuse boundaries. In the second stage, the locations of the abnormalities were used to restrict the spatial extent of the fluid region, and a fuzzy level set method with a spatial smoothness constraint was applied to subretinal fluid segmentation in the SD-OCT scans. Results: Experimental results from 31 retinal SD-OCT volumes with CSC demonstrate that our method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), and positive predicative value (PPV) of 94.3%, 0.97%, and 93.6%, respectively, for NRD regions. Our approach can also discriminate NRD-associated subretinal fluid from subretinal pigment epithelium fluid associated with pigment epithelial detachment with a TPVF, FPVF, and PPV of 93.8%, 0.40%, and 90.5%, respectively. Conclusion: We report a fully automatic method for the segmentation of subretinal fluid. Significance: Our method shows the potential to improve clinical therapy for CSC. [ABSTRACT FROM PUBLISHER]

Objective: We propose an effective automatic method for identification of four retinal layer boundaries from the spectral domain optical coherence tomography images in the presence and absence of pathologies and morphological changes due to disease. Methods: The approach first finds an approximate location of three reference layers and then uses these to bound the search space for the actual layers, which is achieved by modeling the problem as a graph and applying Dijkstra's shortest path algorithm. The edge weight between nodes is determined using pixel distance, slope similarity to a reference, and nonassociativity of the layers, which is designed to overcome the distorting effects that pathology can play in the boundary determination. Results: The accuracy of our method was evaluated on three different datasets. It outperforms the current five state-of-the-art methods. On average, the mean and standard deviation of the root-mean-square error in the form of mean $\pm$ standard deviation in pixels for our method is 1.57 $\pm$ 0.69, which is lower than compared to the existing top five methods of 16.17 $\pm$ 22.64, 6.66 $\pm$ 9.11, 5.70 $\pm$ 10.54, 3.69 $\pm$ 2.04, and 2.29 $\pm$ 1.54. Conclusion: Our method is highly accurate, robust, reliable, and consistent. This identification can enable to quantify the biomarkers of the retina in large-scale study for assessing, monitoring disease progression, as well as early detection of retinal diseases. Significance: Identification of these boundaries can help to determine the loss of neuroretinal cells or layers and the presence of retinal pathology, which can be used as features for the automatic determination of the stages of retinal diseases. [ABSTRACT FROM AUTHOR]

Goal: Reliable recognition of microaneurysms (MAs) is an essential task when developing an automated analysis system for diabetic retinopathy (DR) detection. In this study, we propose an integrated approach for automated MA detection with high accuracy. Methods: Candidate objects are first located by applying a dark object filtering process. Their cross-section profiles along multiple directions are processed through singular spectrum analysis. The correlation coefficient between each processed profile and a typical MA profile is measured and used as a scale factor to adjust the shape of the candidate profile. This is to increase the difference in their profiles between true MAs and other non-MA candidates. A set of statistical features of those profiles is then extracted for a K-nearest neighbor classifier. Results: Experiments show that by applying this process, MAs can be separated well from the retinal background, the most common interfering objects and artifacts. Conclusion: The results have demonstrated the robustness of the approach when testing on large scale datasets with clinically acceptable sensitivity and specificity. Significance: The approach proposed in the evaluated system has great potential when used in an automated DR screening tool or for large scale eye epidemiology studies. [ABSTRACT FROM AUTHOR]

This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2–95.35% vessel segmentation accuracy with 0.9577–0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets. [ABSTRACT FROM PUBLISHER]

Objective: Glaucoma is an irreversible chronic eye disease that leads to vision loss. As it can be slowed down through treatment, detecting the disease in time is important. However, many patients are unaware of the disease because it progresses slowly without easily noticeable symptoms. Currently, there is no effective method for low-cost population-based glaucoma detection or screening. Recent studies have shown that automated optic nerve head assessment from 2-D retinal fundus images is promising for low-cost glaucoma screening. In this paper, we propose a method for cup to disc ratio (CDR) assessment using 2-D retinal fundus images. Methods: In the proposed method, the optic disc is first segmented and reconstructed using a novel sparse dissimilarity-constrained coding (SDC) approach which considers both the dissimilarity constraint and the sparsity constraint from a set of reference discs with known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to compute the CDR for the testing disc. Results: The proposed method has been tested for CDR assessment in a database of 650 images with CDRs manually measured by trained professionals previously. Experimental results show an average CDR error of 0.064 and correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-the-art methods. Our proposed method has also been tested for glaucoma screening. The method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images, respectively, outperforming other methods. Conclusion: The proposed method achieves good accuracy for glaucoma detection. Significance: The method has a great potential to be used for large-scale population-based glaucoma screening. [ABSTRACT FROM PUBLISHER]

Distinctive feature detection is an essential task in computer-assisted minimally invasive surgery (MIS). For special conditions in an MIS imaging environment, such as specular reflections and texture homogeneous areas, the feature points extracted by general feature point detectors are less distinctive and repeatable in MIS images. We observe that abundant blood vessels are available on tissue surfaces and can be extracted as a new set of image features. In this paper, two types of blood vessel features are proposed for endoscopic images: branching points and branching segments. Two novel methods, ridgeness-based circle test and ridgeness-based branching segment detection are presented to extract branching points and branching segments, respectively. Extensive in vivo experiments were conducted to evaluate the performance of the proposed methods and compare them with the state-of-the-art methods. The numerical results verify that, in MIS images, the blood vessel features can produce a large number of points. More importantly, those points are more robust and repeatable than the other types of feature points. In addition, due to the difference in feature types, vessel features can be combined with other general features, which makes them new tools for MIS image analysis. These proposed methods are efficient and the code and datasets are made available to the public. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM PUBLISHER]