Retinal image analysis: preprocessing and feature extraction

Marrugo, Andrés G.; Millán, Marı́a S.

doi:10.1088/1742-6596/274/1/012039

Cited by 29 publications

(20 citation statements)

References 7 publications

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“…In the subsequent phases, the refinement techniques results in the removal of some other parts that contain useful information about the retina health. This approach is also used in (Marrugo and Millan 2011) but reinforced by a principle component analysis (PCA) model to enhance the detection of the foreground objects and avoid any bias to the background region.…”

Section: Problem Statement and Motivationmentioning

confidence: 99%

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

Hayajneh¹,

Rashad²,

Saraereh³

et al. 2016

CIS

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The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process. The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.

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Section: Problem Statement and Motivationmentioning

confidence: 99%

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

Hayajneh¹,

Rashad²,

Saraereh³

et al. 2016

CIS

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“…The novelty in our approach is in the strategy based on eye-domain knowledge for identifying the nonvalid local PSFs and replacing them with appropriate ones. Even though methods for processing retinal images in a spacedependent way (like locally adaptive filtering techniques 15,16 ) have been proposed in the literature; to the best of our knowledge, this is the first time a method for SV deblurring of retinal images is proposed.…”

Section: Contributionmentioning

confidence: 99%

Restoration of retinal images with space-variant blur

et al. 2014

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Abstract. Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of view. Most image deblurring algorithms assume a space-invariant blur, which fails in the presence of space-variant (SV) blur. In this work, we propose an innovative strategy for the restoration of retinal images in which we consider the blur to be both unknown and SV. We model the blur by a linear operation interpreted as a convolution with a point-spread function (PSF) that changes with the position in the image. To achieve an artifact-free restoration, we propose a framework for a robust estimation of the SV PSF based on an eye-domain knowledge strategy. The restoration method was tested on artificially and naturally degraded retinal images. The results show an important enhancement, significant enough to leverage the images' clinical use.

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“…This algorithm was described in Ref. 15 and is based on a simple model of degradation proposed by Foracchia et al 16 The main idea is that the image can be enhanced by estimating the background luminosity and contrast distribution in order to compensate for uneven illumination. Therefore, the enhanced image U (x, y) is expressed as:…”

Section: Preprocessing Of Retinal Imagesmentioning

confidence: 99%

Image analysis in modern ophthalmology: from acquisition to computer assisted diagnosis and telemedicine

Marrugo¹,

Millán

Cristóbal

et al. 2012

SPIE Proceedings

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Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract information about many diseases. Modern ophthalmology thrives and develops on the advances in digital imaging and computing power. In this work we present an overview of recent image processing techniques proposed by the authors in the area of digital eye fundus photography. Our applications range from retinal image quality assessment to image restoration via blind deconvolution and visualization of structural changes in time between patient visits. All proposed within a framework for improving and assisting the medical practice and the forthcoming scenario of the information chain in telemedicine.

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Retinal image analysis: preprocessing and feature extraction

Cited by 29 publications

References 7 publications

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

Restoration of retinal images with space-variant blur

Image analysis in modern ophthalmology: from acquisition to computer assisted diagnosis and telemedicine

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