GMM clustering based segmentation and optic nervehead geometry detection from OCT nervehead images

Paul, Dincy; Priya, S Sathiya; Kumar, T. Ashok

doi:10.1109/gcct.2015.7342687

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…However, the performance of these algorithms is compared with manual annotation by experts or to previous DL or ML approaches segmentation rather than to manufacturers' software. (Marques et al, 2022; Rahman et al, 2021; Devalla et al, 2020; Heisler et al, 2020; Belghith et al, 2014; Yu et al, 2018; Paul et al, 2015; Fu et al, 2015; Antony et al, 2014; Lee et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Influence of signal‐to‐noise ratio, glaucoma stage and segmentation algorithm on OCT usability for quantifying layer thicknesses in the peripapillary retina

Heikka

Jansonius

2022

Acta Ophthalmologica

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Section: Discussionmentioning

confidence: 99%

Influence of signal‐to‐noise ratio, glaucoma stage and segmentation algorithm on OCT usability for quantifying layer thicknesses in the peripapillary retina

Heikka

Jansonius

2022

Acta Ophthalmologica

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“…Moreover, the publicly available dataset, namely, DRION was used to test the proposed system. Paul et al 26 proposed a Gaussian Mixture Model (GMM) to segment the Glaucoma from the Optic Coherence Tomography (OCT) images. In this paper, the ratio of the central portion of the optic nerve was estimated for Glaucoma diagnosis.…”

Section: Segmentation and Clusteringmentioning

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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Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review

Marques

Jesus

Breda

et al. 2022

Computer Methods and Programs in Biomedicine

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GMM clustering based segmentation and optic nervehead geometry detection from OCT nervehead images

Cited by 3 publications

References 9 publications

Influence of signal‐to‐noise ratio, glaucoma stage and segmentation algorithm on OCT usability for quantifying layer thicknesses in the peripapillary retina

Influence of signal‐to‐noise ratio, glaucoma stage and segmentation algorithm on OCT usability for quantifying layer thicknesses in the peripapillary retina

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

Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review

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