Glaucoma detection by using Pearson-R correlation filter

Vijapur, Nataraj; Kunte, Srinivasa Rao R.

doi:10.1109/iccsp.2015.7322695

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…Localization of the optic disc in the retinal image and extraction of the features are done by correlating the fundus image with a newly developed template using Pearson-r correlation. Segmentation of the optic cup and disc is done on the basis of correlation levels [15][16][17][18].…”

Section: Glaucoma Detection Using Templatementioning

confidence: 99%

Efficient Computer-Aided Techniques to Detect Glaucoma

Vijapur¹,

Kunte²

2020

Visual Impairment and Blindness - What We Know and What We Have to Know

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A survey of the World Health Organization has revealed that retinal eye disease Glaucoma is the second leading cause for blindness worldwide. It is a disease which will steals the vision of the patient without any warning or symptoms. About half of the World Glaucoma Patients are estimated to be in Asia. Hence, for social and economic reasons, Glaucoma detection is necessary in preventing blindness and reducing the cost of surgical treatment of the disease. The objective of the chapter is to predict and detect Glaucoma efficiently using image processing techniques. We have developed an efficient computer-aided Glaucoma detection system to classify a fundus image as either normal or glaucomatous image based on the structural features of the fundus image such as cup-to-disc ratio (CDR), rim-to-disc ratio (RDR), superior and inferior neuroretinal rim thicknesses, vessel structure-based features, and distribution of texture features in the fundus images. An automated clinical support system is developed to assist the ophthalmologists to identify the persons who are at risk in the early stages of the disease, monitor the progression of the disease, and minimize the examination time.

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Section: Glaucoma Detection Using Templatementioning

confidence: 99%

Efficient Computer-Aided Techniques to Detect Glaucoma

Vijapur¹,

Kunte²

2020

Visual Impairment and Blindness - What We Know and What We Have to Know

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“…K-mean clustering algorithm and support vector machine (SVM) have also shown reasonable classification accuracy for disc and cup segmentation (Jose and Balakrishnan, 2015;Patil et al, 2015). Pearson-R coefficients features of fundus image were used by Vijapur and Kunte (2015) to distinguish cup pixels from disc region and obtained less classification rate. Some other clustering-based algorithms such as super pixel and simple linear iterative were utilised by Alghmdi et al (2015) for the segmentation of OC and OD regions using fundus images and reported reasonable results.…”

Section: Introductionmentioning

confidence: 99%

Detection of glaucoma based on cup-to-disc ratio using fundus images

Qureshi¹,

Khan²,

Sharif³

et al. 2020

IJISTA

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Glaucoma is a permanent damage of optic nerves which cause of partial or complete visual loss. This work presents a glaucoma detection scheme by measuring CDR from fundus photographs. The proposed system consists of image acquisition, feature extraction and glaucoma assessment steps. Image acquisition discusses the transformation of a RGB fundus image into grey form and enhancing the contrast of fundus features. While, boundary of optic disc and cup were segmented in feature extraction step. Finally, a cup-to-disc ratio of an exploited image will compute to assess glaucoma in the image. The proposed system is tested on 398 fundus images from four publicly available datasets, obtaining an average value of sensitivity 90.6%, specificity 97% and accuracy 96.1% in glaucoma diagnosis. The achieved results show the suitability of proposed art for glaucoma detection.

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“…In this paper, the normal and Glaucoma images were classi¯ed with the help of energy signatures obtained from 2D discrete wavelet transform. Vijapur and Kunte 18 suggested a pearson-R correlation¯ltering technique to detect Glaucoma. Here, the vertical diameters of optic-cup and disc were estimated to accurately segment the image.…”

Section: Preprocessingmentioning

confidence: 99%

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

Balakrishnan

2017

J. Innov. Opt. Health Sci.

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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.

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Glaucoma detection by using Pearson-R correlation filter

Cited by 11 publications

References 9 publications

Efficient Computer-Aided Techniques to Detect Glaucoma

Efficient Computer-Aided Techniques to Detect Glaucoma

Detection of glaucoma based on cup-to-disc ratio using fundus images

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

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