Detecting the Optic Disc and Optic Cup Boundary for Glaucoma Screening A Review

Glaucoma is the leading cause of irreversible blindness in the world. Assessment of damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing for glaucoma screening. This paper present here the automatic glaucoma screening using CDR from 2 D fundus images using superpixel classification. . We compute centre surround statistics from super pixels and unify them with histograms for disc and cup segmentation. Based on the segmented disc and cup, CDR is computed for glaucoma screening. In addition, the proposed method computes a self - assessment reliability score for its disc segmentation result

Factors Influencing Optical Coherence Tomography Peripapillary Choroidal Thickness: A Multicenter Study.

Purpose:To quantify peripapillary choroidal thickness (PCT) and the factors that influence it in healthy participants who represent the racial and ethnic composition of the U.S. population. Methods:A total of 362 healthy participants underwent optical coherence tomography (OCT) enhanced depth imaging of the optic nerve head with a 24 radial B-scan pattern aligned to the fovea to Bruch's membrane opening axis. Bruch's membrane, anterior scleral canal opening (ASCO), and the anterior scleral surface were manually segmented. PCT was measured at 100, 300, 500, 700, 900, and 1100 μm from the ASCO globally and within 12 clock-hour sectors. The effects of age, axial length, intraocular pressure, ethnicity, sex, sector, and ASCO area on PCT were assessed by ANOVA and univariable and multivariable regressions. Results:Globally, PCT was thicker further from the ASCO border and thinner with older age, longer axial length, larger ASCO area, European descent, and female sex. Among these effectors, age and axial length explained the greatest proportion of variance. The rate of age-related decline increased further from the ASCO border. Sectorally, the inferior-temporal sectors were thinnest (10.7%-20.0% thinner than the thickest sector) and demonstrated a higher rate of age-related loss (from 15.6% to 20.7% faster) at each ASCO distance. Conclusions:In healthy eyes, PCT was thinnest in the inferior temporal sectors and thinner PCT was associated with older age, European descent, longer axial length, larger ASCO area, and female sex. Among these associations, age had the strongest influence, and its effect was greatest within the inferior temporal sectors

Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

PurposeThe purpose of this study was to compare retinal nerve fiber layer (RNFL) thickness and optical coherence tomography angiography (OCT-A) retinal vasculature measurements in healthy, glaucoma suspect, and glaucoma patients.MethodsTwo hundred sixty-one eyes of 164 healthy, glaucoma suspect, and open-angle glaucoma (OAG) participants from the Diagnostic Innovations in Glaucoma Study with good quality OCT-A images were included. Retinal vasculature information was summarized as a vessel density map and as vessel density (%), which is the proportion of flowing vessel area over the total area evaluated. Two vessel density measurements extracted from the RNFL were analyzed: (1) circumpapillary vessel density (cpVD) measured in a 750-μm-wide elliptical annulus around the disc and (2) whole image vessel density (wiVD) measured over the entire image. Areas under the receiver operating characteristic curves (AUROC) were used to evaluate diagnostic accuracy.ResultsAge-adjusted mean vessel density was significantly lower in OAG eyes compared with glaucoma suspects and healthy eyes. (cpVD: 55.1 ± 7%, 60.3 ± 5%, and 64.2 ± 3%, respectively; P < 0.001; and wiVD: 46.2 ± 6%, 51.3 ± 5%, and 56.6 ± 3%, respectively; P < 0.001). For differentiating between glaucoma and healthy eyes, the age-adjusted AUROC was highest for wiVD (0.94), followed by RNFL thickness (0.92) and cpVD (0.83). The AUROCs for differentiating between healthy and glaucoma suspect eyes were highest for wiVD (0.70), followed by cpVD (0.65) and RNFL thickness (0.65).ConclusionsOptical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes. These results suggest that OCT-A measurements reflect damage to tissues relevant to the pathophysiology of OAG

Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them

Joint segmentation and classification of retinal arteries/veins from fundus images

Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree. Results The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin

Deep-Layer Microvasculature Dropout by Optical Coherence Tomography Angiography and Microstructure of Parapapillary Atrophy.

Purpose:To investigate the association between the microstructure of β-zone parapapillary atrophy (βPPA) and parapapillary deep-layer microvasculature dropout assessed by optical coherence tomography angiography (OCT-A). Methods:Thirty-seven eyes with βPPA devoid of the Bruch's membrane (BM) (γPPA) ranging between completely absent and discontinuous BM were matched by severity of the visual field (VF) damage with 37 eyes with fully intact BM (βPPA+BM) based on the spectral-domain (SD) OCT imaging. Parapapillary deep-layer microvasculature dropout was defined as a dropout of the microvasculature within choroid or scleral flange in the βPPA on the OCT-A. The widths of βPPA, γPPA, and βPPA+BM were measured on six radial SD-OCT images. Prevalence of the dropout was compared between eyes with and without γPPA. Logistic regression was performed for evaluating association of the dropout with the width of βPPA, γPPA, and βPPA+BM, and the γPPA presence. Results:Eyes with γPPA had significantly higher prevalence of the dropout than did those without γPPA (75.7% versus 40.8%; P = 0.004). In logistic regression, presence and longer width of the γPPA, worse VF mean deviation, and presence of focal lamina cribrosa defects were significantly associated with the dropout (P < 0.05), whereas width of the βPPA and βPPA+BM, axial length, and choroidal thickness were not (P > 0.10). Conclusions:Parapapillary deep-layer microvasculature dropout was associated with the presence and larger width of γPPA, but not with the βPPA+BM width. Presence and width of the exposed scleral flange, rather than the retinal pigmented epithelium atrophy, may be associated with deep-layer microvasculature dropout

An Efficient Approach of Optic Disc Normalization and Segmentation for Glaucoma Detection

Glaucoma is considered as one of the major eye disease which will lead to vision loss if it is not diagnosed at a right time. Hence it is required to recognize the stage of the disease as early as possible. The earlier methods called Intra ocular pressure(IOP) and Visual Field Test have a disadvantage of requirement of special equipment which will be available in only specialized hospitals and provide low accuracy. In this paper effective method called Sparse Dissimilarity Constrained Coding (SDC) have been used where it considers optic disc and cup called cup to disc ratio (CDR). In this approach the optic disc is localized and segmented which is followed by cup segmentation. From which the area of optic disc and optic cup is obtained. The method gives accurate CDR results and it is well suited for more population. From the obtained ratio the stage of the disease can be well predicted and suitable treatment required can be suggested. The retinal fundus images that are used for the method will be easily available in almost all the hospitals and medical centers for comparing the result with the reference CDR ratios. The method provides efficient and reliable result compared to the manual method. Hence the proposed method is an effective approach for glaucoma detection

CUP and DISC OPTIC Segmentation Using Optimized Superpixel Classification for Glaucoma Screening

Glaucoma, an incurable disease related to eyes which results in loss of the vision. Identifying this disease within in a proper period of time is most important, since it cannot be cured. The important aspect of this paper is to detect glaucoma at initial stages. Segmentation in the optic disc necessitates the differentiation of each super pixel by employing Histograms, centre surround statistics. Information location in merged with the above methods in increasing the performance of optic cup segmentation. Optic disc and optic cups are employed to evaluate cup to disc ratio of the disease identified. Neural network is used to extract the patterns and also to detect glaucomatous cells that are too complex to be noticed by either humans or other computer techniques