Jan Odstrcilík

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Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a noreference quality metric to quantify image noise and blur and its application to(More)
The contribution aims at designing and testing an automatic method to estimate the status of the retinal neural fibre layer (NFL) based on analysing the output of the most common ophthalmological imaging modality - fundus camera images. As the neural layer manifests itself in these images rather faintly and is often hardly visible, the method has to utilise(More)
A method for correction of non-uniform illumination in colour fundus images based on the B-spline approximation of the illumination surface is presented. The control points for B-splines are determined from an original image, separately from the red, green and blue channel. The estimated illumination surface is used in a multiplicative model for fast(More)
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(More)
The texture analysis of the retinal nerve fiber layer (RNFL) in colour fundus images is a promising tool for early glaucoma diagnosis. This paper describes model-based method for detection of changes in the RNFL. The method utilizes Gaussian Markov random fields (GMRF) and the least-square error (LSE) estimate for the local RNFL texture modelling. The model(More)
Since images of ocular fundus are commonly available in ophthalmic practice, automatic assessment of the retinal nerve fiber layer (RNFL) can be useful for early diagnosis of glaucoma. This contribution presents a texture analysis method for the description of RNFL status and proposes appropriate textural features for medical diagnosis. The method uses(More)
The retinal ganglion axons are an important part of the visual system, which can be directly observed by fundus camera. The layer they form together inside the retina is the retinal nerve fiber layer (RNFL). This paper describes results of a texture RNFL analysis in color fundus photographs and compares these results with quantitative measurement of RNFL(More)