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

A Polar Map Based Approach Using Retinal Fundus Images for Glaucoma Detection

Cup-to-disc ratio is commonly used as an important parameter for glaucoma screening, involving segmentation of the optic cup on fundus images. We propose a novel polar map representation of the optic disc, using a combination of supervised and unsupervised cup segmentation techniques, for detection of glaucoma. Instead of performing hard thresholding on the segmentation output to extract the cup, we consider the cup confidence scores inside the disc to construct a polar map, and extract sector-wise features for learning a glaucoma risk probability (GRP) for the image. We compare the performance of GRP vis-à-vis the cup-to-disc ratio (CDR). On an evaluation dataset of 100 images from the publicly available RIM-ONE database, our method achieves 82% sensitivity at 84% specificity, and 96% sensitivity at 60% specificity (AUC of 0.8964). Experiments indicate that the polar map based method can provide a more discriminatory glaucoma risk probability score compared to CDR

A Depth Based Approach to Glaucoma Detection Using Retinal Fundus Images

Qualitative evaluation of stereo retinal fundus images by experts is a widely accepted method for optic nerve head evaluation (ONH) in glaucoma. The quantitative evaluation using stereo involves depth estimation of the ONH and thresholding of depth to extract optic cup. In this paper, we attempt the reverse, by estimating the disc depth using supervised and unsupervised techniques on a single optic disc image. Our depth estimation approach is evaluated on the INSPIRE-stereo dataset by using single images from the stereo pairs, and is compared with the OCT based depth ground truths. We extract spatial and intensity features from the depth maps, and perform classification of images into glaucomatous and normal. Our approach is evaluated on a dataset of 100 images and achieves an AUC of 0.888 with a sensitivity of 83% at specificity 83%. Experiments indicate that our approach can reliably estimate depth, and provide valuable information for glaucoma detection and for monitoring its progression

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