Andrés G. Marrugo

Retinal image restoration by means of blind deconvolution

Retinal imaging plays a key role in the diagnosis and management of ophthalmologic disorders, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Because of the acquisition process, retinal images often suffer from blurring and uneven illumination. This problem may seriously affect disease diagnosis and progression assessment. Here we present a method for color retinal image restoration by means of multichannel blind deconvolution. The method is applied to a pair of retinal images acquired within a lapse of time, ranging from several minutes to months. It consists of a series of preprocessing steps to adjust the images so they comply with the considered degradation model, followed by the estimation of the point-spread function and, ultimately, image deconvolution. The preprocessing is mainly composed of image registration, uneven illumination compensation, and segmentation of areas with structural changes. In addition, we have developed a procedure for the detection and visualization of structural changes. This enables the identification of subtle developments in the retina not caused by variation in illumination or blur. The method was tested on synthetic and real images. Encouraging experimental results show that the method is capable of significant restoration of degraded retinal images.

Retinal image analysis: preprocessing and feature extraction

Image processing, analysis and computer vision techniques are found today in all fields of medical science. These techniques are especially relevant to modern ophthalmology, a field heavily dependent on visual data. Retinal images are widely used for diagnostic purposes by ophthalmologists. However, these images often need visual enhancement prior to apply a digital analysis for pathological risk or damage detection. In this work we propose the use of an image enhancement technique for the compensation of non-uniform contrast and luminosity distribution in retinal images. We also explore optic nerve head segmentation by means of color mathematical morphology and the use of active contours.

Restoration of retinal images with space-variant blur

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.

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

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.

Anisotropy-based robust focus measure for non-mydriatic retinal imaging.

Non-mydriatic retinal imaging is an important tool for diagnosis and progression assessment of ophthalmic diseases. Because it does not require pharmacological dilation of the patients pupil, it is essential for screening programs performed by non-medical personnel. A typical camera is equipped with a manual focusing mechanism to compensate for the refractive errors in the eye. However, manual focusing is error prone, especially when performed by inexperienced photographers. In this work, we propose a new and robust focus measure based on a calculation of image anisotropy which, in turn, is evaluated from the directional variance of the normalized discrete cosine transform. Simulation and experimental results demonstrate the effectiveness of the proposed focus measure.

No-reference quality metrics for eye fundus imaging

This paper presents a comparative study on the use of noreference quality metrics for eye fundus imaging. We center on autofocusing and quality assessment as key applications for the correct operation of a fundus imaging system. Four state-of-the-art no-reference metrics were selected for the study. From these, a metric based of Renyi anisotropy yielded the best performance in both auto-focusing and quality assessment.

Improving the blind restoration of retinal images by means of point-spread-function estimation assessment

Retinal images often suffer from blurring which hinders disease diagnosis and progression assessment. The restoration of the images is carried out by means of blind deconvolution, but the success of the restoration depends on the correct estimation of the point-spread-function (PSF) that blurred the image. The restoration can be space-invariant or space-variant. Because a retinal image has regions without texture or sharp edges, the blind PSF estimation may fail. In this paper we propose a strategy for the correct assessment of PSF estimation in retinal images for restoration by means of space-invariant or space-invariant blind deconvolution. Our method is based on a decomposition in Zernike coefficients of the estimated PSFs to identify valid PSFs. This significantly improves the quality of the image restoration revealed by the increased visibility of small details like small blood vessels and by the lack of restoration artifacts.

Toward computer-assisted diagnosis and telemedicine in ophthalmology

Digital retinal imaging is an important element of modern ophthalmology. It provides visual documentation and, most importantly, the ability to extract and process information automatically. It is difficult to conceive modern health care practices without digital imaging and electronic health records. They both have led to a significant improvement in health care quality, but at the expense of lower physician productivity. In the words of Michael Abramoff,1 a leading specialist in the field, “health-care automation has made physicians maybe do better, but not more.” However, this seems to be merely the beginning of what is possible. A general lack of resources alongside ever-increasing health care costs is bound to stagnate this impetus for further improving the quality of health care, or most likely continue to leave an incredible amount of patients undiagnosed or untreated. In the long term, this translates into further increases in health care expenditures because, if a patient is left untreated, the cost of medical care in advanced stages of a disease increases dramatically. This situation could be avoided by investing in proper screening mechanisms. It is within this overwhelming context that digital image analysis techniques can be employed to overcome most of the problems associated with eye disease screening, management, and progression assessment, among others. Computer-aided diagnosis (CAD) and telemedicine use has risen as the integration of different technological efforts is aimed at overcoming these difficulties. Retinal image analysis is a continuously growing research field with newly completed results being translated into clinical use. However, there are still many barriers to overcome before a definitive successful clinical Figure 1. Illumination compensation algorithm.

Multichannel blind deconvolution in eye fundus imaging

Eye fundus imaging is vital for modern ophthalmology. Due to the acquisition process, fundus images often suffer from blurring and uneven illumination. This hinders diagnosis and the evolution assessment of a disease. We present a method for fundus image deblurring by means of multichannel blind deconvolution. It consists of a series of preprocessing steps to adjust the images so they comply with the considered degradation model, followed by the estimation of the point spread function, and image deconvolution. Results show that our approach is capable of significant resolution improvement in degraded retinal images.

A Structure-from-Motion Pipeline for Topographic Reconstructions Using Unmanned Aerial Vehicles and Open Source Software

In recent years, the generation of accurate topographic reconstructions has found applications ranging from geomorphic sciences to remote sensing and urban planning, among others. The production of high resolution, high-quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware, and software. Photogrammetry offers clear advantages over other methods of collecting geomatic information. Airborne cameras can cover large areas more quickly than ground survey techniques, and the generated Photogrammetry-based DEMs often have higher resolution than models produced with other remote sensing methods such as LIDAR (Laser Imaging Detection and Ranging) or RADAR (radar detection and ranging).