Detection of Glaucoma Using Cup to Disc Ratio From Spectral Domain Optical Coherence Tomography Images

Khalil, Tehmina; Akram, M. Usman; Raja, Hina; Jameel, Amina; Basit, Imran

doi:10.1109/access.2018.2791427

Cited by 46 publications

(11 citation statements)

References 28 publications

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“…Finally, a comparative table is also presented for readers. Khalil et al,2018 experimented on SD-OCT images; they have used ILM (inner limited membrane) as a parameter which gives accurate results even in the poor visualization. They use the ILM process to generate solid contour that is used to generate accurate cup diameter.…”

Section: Literature Surveymentioning

confidence: 99%

An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images

Singh

Pooja

Garg

et al. 2021

International Journal of E-Health and Medical Communications

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Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. Forty-five vital features were extracted using two approaches from the OCT images. K-nearest neighbor (KNN), linear discriminant analysis (LDA), decision tree, random forest, support vector machine (SVM) were applied for the categorization of OCT images among the glaucomatous and non-glaucomatous class. The largest AUC is achieved by KNN (0.97). The accuracy is obtained on fivefold cross-validation techniques. This study will facilitate to reach high standards in glaucoma diagnosis.

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Section: Literature Surveymentioning

confidence: 99%

An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images

Singh

Pooja

Garg

et al. 2021

International Journal of E-Health and Medical Communications

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“…Khalil et al [13] evaluated the works done under the general term of content location in video and ordered them as feature based, format based, classifier based and movement put together without any requirements with respect to camera movement. In the study, it has been acquainted different division strategies important with following contents in video and sorted item following into pixel extraction, content extraction and outline following and looked at the techniques in every classification.…”

Section: Literature Surveymentioning

confidence: 99%

Feature Extraction Model with Group-Based Classifier for Content Extraction from Video Data

Kalakoti¹,

Prabakaran²

2021

RIA

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In today's PC illustration, numerous object locations of videos are quite critical duties to accomplish. Swiftly and reliably recognising and distinguishing the multiple aspects of a video is a crucial attribute for collaborating with one's condition (object). The core issue is that in theory, to ensure that no significant aspect is missing; all aspects of a content in a video must be scanned for elements on various different scales. It requires some investment and effort anyway, to really arrange the substance of a given content region and both time and computational limits that an operator can spend on classification are constrained. Two presumption procedures for accelerating the standard identifier are performed by the proposed method and demonstrate their capability by performing both identification efficiency and velocity. The main enhancement of our group-based classifier focuses on accelerating the grouping of sub features by planning the problem as a selection procedure for consecutive features. The subsequent improvement gives better multiscale features to distinguish objects of all sizes without rescaling the information image from a video. Extracting contents from video is an assortment of successive images with a steady time interim. So video can give more data about contents in it when situations are changing regarding time. Along these lines, physically taking care of contents with features are very unimaginable. In the proposed work, it is suggested that a Group-based Video Content Extraction Classifier (GbCCE) extracts content from a video by extracting relevant features using a group-based classifier. The proposed method is distinct from conventional approaches and the findings indicate that better output is demonstrated by the proposed method.

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“…Thresholding algorithms segment the image based on quantifiable features like image intensity or gradient magnitude, and divide the pixels according to a defined threshold. Ramzan et al in [Ramzan et al, 2018] and [Ramzan et al, 2019], and Khalil et al [Khalil et al, 2018] proposed methods to extract the cup to disc ratio (CDR) through the segmentation of the ILM using intensity thresholds. In [Ramzan et al, 2018], the ILM was extracted using intensity based thresholding, followed by interpolation to fill in the gaps.…”

Section: Thresholdingmentioning

confidence: 99%

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

Marques¹,

Jesus²,

Breda³

et al. 2021

Preprint

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The optic nerve head (ONH) represents the intraocular section of the optic nerve, which is prone to damage by intraocular pressure (IOP). The advent of optical coherence tomography (OCT) has enabled the evaluation of novel ONH parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane minimum-rim-width (BMO-MRW), these seem to be promising ONH parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these OCT derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of ONH in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 27 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analyzed. The results show that deep learning-based algorithms provide the highest accuracy, sensitivity and specificity for segmenting the different structures of the ONH including the LC. However, a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches has been observed, highlighting the importance and need of standardized methodologies for ONH segmentation.

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Detection of Glaucoma Using Cup to Disc Ratio From Spectral Domain Optical Coherence Tomography Images

Cited by 46 publications

References 28 publications

An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images

An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images

Feature Extraction Model with Group-Based Classifier for Content Extraction from Video Data

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

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