Retrospective Illumination Correction of Retinal Images

Kubecka, Libor; Jan, Jiří; Kolář, Radim

doi:10.1155/2010/780262

Cited by 23 publications

(17 citation statements)

References 29 publications

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“…A better contrast of the relevant objects on the retina such as the blood vessels or the optic disk can be obtained by restoring the uniformity of the retina illumination. The classic methods estimate the illumination or the bias field, using the commonly used terminology, and subtract it from the original image to obtain the final result [9], [8]. Figure 6 shows an example of a retina image and the result after applying our model.…”

Section: Resultsmentioning

confidence: 99%

Screened Poisson Equation for Image Contrast Enhancement

Morel¹,

Petro²,

Sbert³

2014

Image Processing on Line

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In this work we propose a discussion and detailed implementation of a very simple gradient domain method that tries to eliminate the effect of nonuniform illumination and at the same time preserves the images details. This model, which to the best of our knowledge has not been explored in spite of its simplicity, acts as a high pass filter. We show that with a single contrast parameter (which keeps the same value in most experiments), the model delivers state of the art results. They compare favorably to results obtained with more complex algorithms. Our algorithm is designed for all kinds of images, but with the special specification of making minimal image detail alteration thanks to a first order fidelity term, instead of the usual zero order term. Experiments on non-uniform medical images and on hazy images illustrate significant perception gain. Source Code The source code and the online demo are accessible at the IPOL web page of this article 1. In this link an ANSI C source code is available for download which produces the same results as the demo. Compilation and usage instructions are included in a README.txt file. The online demo allows one to try Screened Poisson Equation on any uploaded image choosing the tradeoff parameter λ and the level of saturation of the simplest color balance s. By default the values of λ and s are 0.0001 and 0.1 respectively.

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Section: Resultsmentioning

confidence: 99%

Screened Poisson Equation for Image Contrast Enhancement

Morel¹,

Petro²,

Sbert³

2014

Image Processing on Line

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show abstract

“…The images were preprocessed for reduction of noise by applying a binary mask and a median filter. Correction of the non-uniform illumination was performed for accurate blood vessel segmentation by enhancing the contrast of the blood vessels at the periphery of the fundus photographs 35. After that, blood vessel removal was done to detect RNFL defects more accurately.…”

Section: Methodsmentioning

confidence: 99%

Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening

Han

Yang

et al. 2016

Br J Ophthalmol

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The new CAD system successfully detected RNFL defects during mass screening of fundus photographs in a large population who visited a healthcare centre. The proposed algorithm can be useful for clinicians in screening RNFL defects in healthcare centres. The false-positive rate is still unsatisfactory, although improved compared with the previous study. Further studies are needed to enhance the speed and specificity of image analysis using the CAD system.

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“…The objective of illumination adjustment is to evacuate uneven illumination of the image caused by sensor defaults, nonuniform illumination of the scene, or introduction of the surface. The known illumination revision techniques in the writing can be classified in the following gatherings: filtering segmentation based, surface fitting, and different strategies [1].…”

Section: A Fundus Image Preprocessingmentioning

confidence: 99%

Computer Aided Optical Disc Detection in Fundus Images: A Review

Balaji¹,

Suryanarayana²,

Kakarla³

2018

CVRJST

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Colour fundus images of the human retina are increasingly used in the diagnosis and treatment of several eye related pathologies and in ailments such as arteriosclerosis, diabetes, and hypertension. The effectiveness of treatment for many eye related diseases lies in the early detection through regular screenings. But, screening a large number of patients is a significant problem faced by medical practitioners in populous developing countries like India. In addition, there are extensive influences of human blunders and subjectivity on the consequences of assessment by a human master. This opens up the probability of applying propelled image processing techniques in fundus images to support and improve determination in different ways. Thus, automatic detection of pathologies and computer aided analysis in retinal images play a important role in modern diagnostic procedures and screening systems. Reliable and robust extraction of retinal features like optic disc, macula and vasculature is an essential for ensuing retinal image analysis and handling since these are the overwhelming and most stable structures showing up in the retina. However, automatic segmentation of retinal images is a complicated affair since retinal images are often poorly contrasted noisy, and there are a wide variations in orientation. This paper presents a review on algorithms or methods to detect optic disc automatically.

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Retrospective Illumination Correction of Retinal Images

Cited by 23 publications

References 29 publications

Screened Poisson Equation for Image Contrast Enhancement

Screened Poisson Equation for Image Contrast Enhancement

Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening

Computer Aided Optical Disc Detection in Fundus Images: A Review

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