scispace - formally typeset
Search or ask a question
Book

Clinical Ophthalmology: A Systematic Approach

01 Jan 1989-
TL;DR: Ocular side-effects of systemic medication 21.1.
Abstract: 1. Eyelids 2. Lacrimal Drainage System 3. Orbit 4. Dry Eye Disorders 5. Conjunctiva 6. Cornea 7. Corneal and Refractive Surgery 8. Episclera and Sclera 9. Lens 10. Glaucoma 11. Uveitis 12. Ocular Tumours 13. Retinal Vascular Disease 14. Acquired Macular Disorders 15. Hereditary Fundus Dystrophies 16. Retinal Detachment 17. Vitreous Opacities 18. Strabismus 19. Neuro-ophthalmology 20. Ocular side-effects of systemic medication 21. Trauma Index
Citations
More filters
Journal ArticleDOI
TL;DR: In this paper, a method for automated segmentation of the vasculature in retinal images is presented, which produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector.
Abstract: We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods

1,435 citations

Journal ArticleDOI
TL;DR: A Bayesian classifier with class-conditional probability density functions described as Gaussian mixtures is used, yielding a fast classification, while being able to model complex decision surfaces, for automated segmentation of the vasculature in retinal images.
Abstract: We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.

859 citations


Cites background from "Clinical Ophthalmology: A Systemati..."

  • ...Inspection of the retinal vasculature may reveal hypertension, diabetes, arteriosclerosis, cardiovascular disease, and stroke [3]....

    [...]

Journal ArticleDOI
TL;DR: In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed and two segmentation methods are considered.
Abstract: In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed. A line detector, previously used in mammography, is applied to the green channel of the retinal image. It is based on the evaluation of the average grey level along lines of fixed length passing through the target pixel at different orientations. Two segmentation methods are considered. The first uses the basic line detector whose response is thresholded to obtain unsupervised pixel classification. As a further development, we employ two orthogonal line detectors along with the grey level of the target pixel to construct a feature vector for supervised classification using a support vector machine. The effectiveness of both methods is demonstrated through receiver operating characteristic analysis on two publicly available databases of color fundus images.

819 citations


Cites background from "Clinical Ophthalmology: A Systemati..."

  • ...DIGITAL fundus imaging in ophthalmology plays an important role in medical diagnosis of several pathologies like hypertension, diabetes, and cardiovascular disease [1]....

    [...]

Proceedings ArticleDOI
01 Oct 2018
TL;DR: A U-Net-like model with the weighted attention mechanism and the skip connection scheme for addressing issues of dealing with small thin vessels, low discriminative ability at the optic disk area, etc is proposed.
Abstract: Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. Recently, deep learning based retinal vessel segmentation methods have reached the state-of-the-art performance. Due to the extreme variations in the morphology of the vessels against the noisy background, these methods still have issues of dealing with small thin vessels, low discriminative ability at the optic disk area, etc. In this paper, we proposed a U-Net-like model with the weighted attention mechanism and the skip connection scheme for addressing these issues. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed methods.

420 citations


Cites background from "Clinical Ophthalmology: A Systemati..."

  • ...INTRODUCTION The retina is the only human body part which can observe the microcirculation through a noninvasive fundus examination, and the retinal vessel can serve as an important signal for diagnosing chronic eye diseases, cardiovascular diseases, and diabetic retinopathy [6]....

    [...]

Journal ArticleDOI
TL;DR: This review examines the epidemiology, frequency, immunology, and immunohistopathology of Behçet disease with recent theories of several agents, including phosphoantigens, superantIGens, heat-shock proteins, and adenosine deaminase with recent insights into the pharmacology and effects of thalidomide, tacrolimus, and soluble TNF receptor.

383 citations