Imaging in Ophthalmology

Yolcu, Ümit; Şahin, Ömer; Gündoğan, Fatih Ç.

doi:10.5772/58314

Cited by 4 publications

(7 citation statements)

References 83 publications

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“…(i) Biomicroscopy: The slit lamp biomicroscopy is a routine clinical examination tool and used to view all ocular structures of eye [21]. It consists of two major systems such as illumination and observation.…”

Section: Diagnostic Imagingmentioning

confidence: 99%

“…It consists of two major systems such as illumination and observation. Illumination system delivers bright, sharp and thin light to the focal plane to view ocular structures [21,22]. Nowadays, LED slit lamps are used in illumination system.…”

Section: Diagnostic Imagingmentioning

confidence: 99%

“…Nowadays, LED slit lamps are used in illumination system. Observation system consists of several optical lenses (5 Â to 40 Â ) to provide magnified view of ocular structures [21,22]. Moreover, it has 10-15°convergence angle to reduce the convergence need of clinicians during eye examination [21,22].…”

Section: Diagnostic Imagingmentioning

confidence: 99%

“…Observation system consists of several optical lenses (5 Â to 40 Â ) to provide magnified view of ocular structures [21,22]. Moreover, it has 10-15°convergence angle to reduce the convergence need of clinicians during eye examination [21,22]. (ii) Fluorescein Angiography: FA imaging is performed by injecting fluorescein into the circulatory system and the images are captured using fundus camera, where the image intensities represents the amount of emitted photons from fluorescein [6,23].…”

Section: Diagnostic Imagingmentioning

confidence: 99%

See 3 more Smart Citations

Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review

Mookiah

Acharya

Fujita

et al. 2015

Computers in Biology and Medicine

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Section: Diagnostic Imagingmentioning

confidence: 99%

Section: Diagnostic Imagingmentioning

confidence: 99%

Section: Diagnostic Imagingmentioning

confidence: 99%

Section: Diagnostic Imagingmentioning

confidence: 99%

See 2 more Smart Citations

Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review

Mookiah

Acharya

Fujita

et al. 2015

Computers in Biology and Medicine

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“…Imaging modalities play a critical role in routine ophthalmologic practice. It is nearly impossible to conduct ophthalmic examination without employing appropriate imaging modalities [15]. Ophthalmic imaging modalities, including slit-lamp images, fundus images, and OCT images have been widely used for cataract detection and grading.…”

Section: Introductionmentioning

confidence: 99%

Towards a Connected Mobile Cataract Screening System: A Future Approach

et al. 2022

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Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.

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Efficient Screening of Diseased Eyes based on Fundus Autofluorescence Images using Support Vector Machine

Manne

Vupparaboina²,

Gudapati

et al. 2021

2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

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A variety of vision ailments are associated with the foveal region of the eye. In current clinical practice, the ophthalmologist manually detects potential presence of such ailments based on fundus autofluorescence (FAF) images, and hence diagnoses the disease, when relevant. However, in view of the general scarcity of ophthalmologists relative to the large number of subjects seeking eyecare, especially in remote regions, it becomes imperative to develop methods to direct expert time and effort to medically significant cases. To serve the interest of both the ophthalmologist and the potential patient, we plan a screening step, where healthy and diseased eyes are algorithmically differentiated with limited input from only optometrists who are relatively more abundant in number. Specifically, an early treatment diabetic retinopathy study (ETDRS) grid is placed by an optometrist on each FAF image, based on which sectoral statistics are automatically collected. Using such statistics as features, healthy and diseased eyes are proposed to be classified by training an algorithm using available medical records. In this connection, we consider support vector machine (SVM) with linear as well as radial basis function (RBF) kernel, and observe satisfactory performance of both variants. Among those, we recommend the latter in view of its slight superiority in terms of classification accuracy (90.55% at a standard training-to-test ratio of 80:20), and practical class-conditional costs.Index Terms-Fundus autofluorescence (FAF), Early treatment diabetic retinopathy study (ETDRS) grid, Support vector machine (SVM), Monte Carlo cross validation (MCCV).

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Imaging in Ophthalmology

Cited by 4 publications

References 83 publications

Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review

Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review

Towards a Connected Mobile Cataract Screening System: A Future Approach

Efficient Screening of Diseased Eyes based on Fundus Autofluorescence Images using Support Vector Machine

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