Optic nerve head segmentation

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred image

Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio( CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN) . A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images

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

Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model

Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes

Superpixel-Based Optic Nerve Head Segmentation Method of Fundus Images for Glaucoma Assessment

Glaucoma disease is the second leading cause of blindness in the world. This progressive ocular neuropathy is mainly caused by uncontrolled high intraocular pressure. Although there is still no cure, early detection and appropriate treatment can stop the disease progression to low vision and blindness. In the clinical practice, the gold standard used by ophthalmologists for glaucoma diagnosis is fundus retinal imaging, in particular optic nerve head (ONH) subjective/manual examination. In this work, we propose an unsupervised superpixel-based method for the optic nerve head (ONH) segmentation. An automatic algorithm based on linear iterative clustering is used to compute an ellipse fitting for the automatic detection of the ONH contour. The tool has been tested using a public retinal fundus images dataset with medical expert ground truths of the ONH contour and validated with a classified (control vs. glaucoma eyes) database. Results showed that the automatic segmentation method provides similar results in ellipse fitting of the ONH that those obtained from the ground truth experts within the statistical range of inter-observation variability. Our method is a user-friendly available program that provides fast and reliable results for clinicians working on glaucoma screening using retinal fundus images

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

Optic Cup Segmentation Using Large Pixel Patch Based CNNs

Optic cup(OC) segmentation on color fundus image is essential for the calculation of cup-to-disk ratio and fundus morphological analysis, which are very important references in the diagnosis of glaucoma. In this paper we proposed an OC segmentation method using convolutional neural networks(CNNs) to learn from big size patch belong to each pixel. The segmentation result is achieved by classification of each pixel patch and postprocessing. With large pixel patch, the network could learn more global information around each pixel and make a better judgement during classification. We tested this method on public dataset Drishti-GS and achieved average F-Score of 93.73% and average overlapping error of 12.25%, which is better than state-of-the-art algorithms. This method could be used for fundus morphological analysis, and could also be employed to other medical image segmentation works which the boundary of the target area is fuzzy

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)