NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Abstract

Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure (IOP), which damages the vision of eyes. So, detecting and classifying Glaucoma is an important and demanding task in recent days. For this purpose, some of the clustering and segmentation techniques are proposed in the existing works. But, it has some drawbacks that include ine±cient, inaccurate and estimates only the a®ected area. In order to solve these issues, a Neighboring Di®erential Clustering (NDC) -Intensity Variation Masking (IVM) are proposed in this paper. The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc. This work includes three stages such as, preprocessing, clustering and segmentation. At¯rst, the given retinal image is preprocessed by using the Gaussian Mask Updated (GMU) model for eliminating the noise and improving the quality of the image. Then, the cluster is formed by extracting the threshold and patterns with the help of NDC technique. In the segmentation stage, the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method. Here, the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identi¯cation. In experiments, the results of both existing and proposed techniques are evaluated in terms of sensitivity, speci¯city, accuracy, Hausdor® distance, Jaccard and dice metrics.

A Fast and Accurate Method for Glaucoma Screening from Smartphone-Captured Fundus Images

“…To facilitate access to early diagnosis, several studies have proposed automatic methods for glaucoma screening, which differ with respect to the glaucoma properties addressed for detection. Several methods such as the ones suggested in [4,5,6,7,8,9,10,39,28,29,33,23] segmented the Optic Disk (OD) and the Optic Cup (OC) in order to deduce the Cup-to-Disc Ratio (CDR). Moreover, the segmented OD and OC were allowed to extract the Neuro-Retinal Rim (NRR) shape where glaucoma could be identified based on the ISNT rule [41].…”

Retinal Image Processing in Biometrics

“…(a) glaucoma impact to the optic nerve head[29]; (b) healthy fundus image; (c) glaucoma fundus imageThe diabetic retinopathy is a microvascular complication that may lead to vision lost. The World Health Organization indicates that 366 million persons can be affected by DR in 2030.…”