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

/pdf/retinal-vessel-segmentation-using-the-2-d-gabor-wavelet-and-3rb2432h14.pdf

Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification

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.

/pdf/retinal-vessel-segmentation-using-the-2-d-morlet-wavelet-and-2td3vuha7b.pdf

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

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.

Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification

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.

Weighted Res-UNet for High-Quality Retina Vessel Segmentation

Current Concepts in the Etiology and Treatment of Behçet Disease

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

Clinical Ophthalmology: A Systematic Approach

Kanski's Clinical Ophthalmology: A Systematic Approach

1. Eyelids 2. Lacrimal Drainage System 3. Orbit 4. Dry Eye 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

Synopsis of Clinical Ophthalmology

Publisher Summary
This chapter discusses the various disorders of the lens. The syndromes associated with cataracts are Down's syndrome, Werner's syndrome, and Rothmund's syndrome. The disadvantages of intracapsular cataract extraction (ICCE) are that it is unsuitable for persons under 35 years of age because of the high risk of vitreous loss as a result of the intact capsulohyaloid ligament, and there is increased risk of pupil block, vitreous touch, vitreous wick, retinal detachment, and cystoid macular edema. The disadvantages of extracapsular cataract extraction (ECCE) are that opacification of the posterior capsule occurs in up to 50% of cases and it cannot be performed if the zonules are disrupted. Incision is the same in ECCE as for ICCE but is slightly smaller. Many small radial cuts are made in the anterior capsule at 360° and the process is called “anterior capsulotomy.” The nucleus is expressed by exerting counterpressure on the sclera at the 6 o'clock and 12 o'clock positions. Cortical cleanup is performed with either a manual or an automated infusion–aspiration device.

Disorders of the lens

Kanski's Clinical Ophthalmology, 8th Edition - Libros de Medicina - Oftalmologia clinica - 175,00

Jack J. Kanski

Papers

Clinical Ophthalmology: A Systematic Approach

Kanski's Clinical Ophthalmology: A Systematic Approach

Synopsis of Clinical Ophthalmology

Disorders of the lens

Kanski's Clinical Ophthalmology, 8th Edition