• MeSH
• dítě MeSH
• lidé MeSH
• poruchy zraku diagnóza   prevence a kontrola   MeSH
• retinoskopie * metody   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH

• MeSH
• lidé MeSH
• refrakce oka fyziologie   MeSH
• retinoskopie * metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• přehledy MeSH

• MeSH
• koronární sinus * abnormality   anatomie a histologie   diagnostické zobrazování   MeSH
• lidé MeSH
• retinoskopie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• přehledy MeSH

AIMS: Retinopathy of Prematurity (ROP) is a potentially serious condition that can afflict preterm infants. Timely and correct identification of individuals at risk of developing a serious form of ROP is therefore of paramount importance. WinROP is an online system for predicting ROP based on birth weight and weight increments. However, the results vary significantly for various populations. It has not been evaluated in the Czech population. This study evaluates the test characteristics (specificity, sensitivity, positive and negative predictive values) of the WinROP system in Czech preterm infants. METHODS: Data on 445 prematurely born infants included in the ROP screening program at the University Hospital Ostrava, Czech Republic, were retrospectively entered into the WinROP system and the outcomes of the WinROP and regular screening were compared. RESULTS: All 24 infants who developed high-risk (Type 1 or Type 2) ROP were correctly identified by the system. The sensitivity and negative predictive values for this group were 100%. However, the specificity and positive predictive values were substantially lower, resulting in a large number of false positives. Extending the analysis to low risk ROP, the system did not provide such reliable results. CONCLUSIONS: The system is a valuable tool for identifying infants who are not likely to develop high-risk ROP and this could help to substantially reduce the number of preterm infants in need of regular ROP screening. It is not suitable for predicting the development of less serious forms of ROP which is however in accordance with the declared aims of the WinROP system.

• MeSH
• gestační stáří MeSH
• lidé MeSH
• novorozenci extrémně nezralí MeSH
• novorozenec nedonošený MeSH
• novorozenec MeSH
• novorozenecký screening metody   normy   MeSH
• retinopatie nedonošených diagnóza   MeSH
• retinoskopie metody   normy   MeSH
• retrospektivní studie MeSH
• senzitivita a specificita MeSH
• Check Tag
• lidé MeSH
• novorozenec MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

• MeSH
• algoritmy * MeSH
• discus nervi optici patologie   MeSH
• dospělí MeSH
• glaukom patologie   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• reprodukovatelnost výsledků MeSH
• retinoskopie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• strojové učení MeSH
• subtrakční technika MeSH
• vlnková analýza MeSH
• vylepšení obrazu metody   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

• MeSH
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• předškolní dítě MeSH
• prevalence MeSH
• refrakční vady * diagnóza   epidemiologie   etiologie   MeSH
• retinoskopie * metody   MeSH
• zrakové testy * metody   přístrojové vybavení   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• předškolní dítě MeSH

An automatic method of segmenting the retinal vessel tree and estimating status of retinal neural fibre layer (NFL) from high resolution fundus camera images is presented. First, reliable blood vessel segmentation, using 2D directional matched filtering, enables to remove areas occluded by blood vessels thus leaving remaining retinal area available to the following NFL detection. The local existence of rather faint and hardly visible NFL is detected by combining several newly designed local textural features, sensitive to subtle NFL characteristics, into feature vectors submitted to a trained neural-network classifier. Obtained binary retinal maps of NFL distribution show a good agreement with both medical expert evaluations and quantitative results obtained by optical coherence tomography.

• MeSH
• fluoresceinová angiografie metody   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• nemoci zrakového nervu patologie   MeSH
• nervová síť patologie   MeSH
• reprodukovatelnost výsledků MeSH
• retinální cévy patologie   MeSH
• retinoskopie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• senzitivita a specificita MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

This paper describes an ongoing project that has the aim to develop a low cost application to replace a computer mouse for people with physical impairment. The application is based on an eye tracking algorithm and assumes that the camera and the head position are fixed. Color tracking and template matching methods are used for pupil detection. Calibration is provided by neural networks as well as by parametric interpolation methods. Neural networks use back-propagation for learning and bipolar sigmoid function is chosen as the activation function. The user’s eye is scanned with a simple web camera with backlight compensation which is attached to a head fixation device. Neural networks significantly outperform parametric interpolation techniques: 1) the calibration procedure is faster as they require less calibration marks and 2) cursor control is more precise. The system in its current stage of development is able to distinguish regions at least on the level of desktop icons. The main limitation of the proposed method is the lack of head-pose invariance and its relative sensitivity to illumination (especially to incidental pupil reflections).

• MeSH
• fotografování metody   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• mladý dospělý MeSH
• neuronové sítě (počítačové) MeSH
• pohyby očí fyziologie   MeSH
• retina anatomie a histologie   fyziologie   MeSH
• retinoskopie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• senzitivita a specificita MeSH
• uživatelské rozhraní počítače MeSH
• Check Tag
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• práce podpořená grantem MeSH

A semiautomatic approach to the detection and evaluation of the autofluorescent zones in retinal images, recognized as having a diagnostic value, has been designed based on fusing information from two Heidelberg Retina Angiograph imaging modalities - autofluorescent and infrared modes. The procedure, initiated by automatic preprocessing and region-of-interest determination continues by manually initiated segmentation via constrained region growing and ends with evaluating the size and geometrical coordinates of the AF regions with respect to the centre of the optic disc. Results are compared with those obtained by experienced ophthalmologists.

• MeSH
• algoritmy MeSH
• financování organizované MeSH
• fluoresceinová angiografie metody   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• reprodukovatelnost výsledků MeSH
• retina anatomie a histologie   MeSH
• retinoskopie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• senzitivita a specificita MeSH
• umělá inteligence MeSH
• vylepšení obrazu metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• hodnotící studie MeSH

The paper describes restitution of geometrical distortions and improvement of signal-to-noise ratio of auto-fluorescence retinal images, finally aimed at segmentation and area estimation of the lipofuscin spots as one of the features to be included in glaucoma diagnosis. The main problems - geometrical and illumination incompatibility of frames in the image sequence and a non-negligible "shear" distortion in the individual frames - have been solved by the presented registration procedure. The concept and some details of the MI-based regularized registration, together with evaluation of test results form the core of the contribution.

• MeSH
• algoritmy MeSH
• financování organizované MeSH
• fluoresceinová angiografie metody   MeSH
• interpretace obrazu počítačem metody   MeSH
• konfokální mikroskopie metody   MeSH
• lidé MeSH
• reprodukovatelnost výsledků MeSH
• retinální cévy cytologie   MeSH
• retinoskopie metody   MeSH
• senzitivita a specificita MeSH
• subtrakční technika MeSH
• vylepšení obrazu metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• hodnotící studie MeSH