Browsing by Author "Millán M.S."

Now showing 1 - 7 of 7

Improving the blind restoration of retinal images by means of point-spread-function estimation assessment
(SPIE, 2015) Marrugo A.G.; Millán M.S.; Šorel M.; Kotera J.; Šroubek F.; Romero E.; Lepore N.
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. © 2015 SPIE.
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting
(Institute of Electrical and Electronics Engineers Inc., 2019) Barrios E.M.; Marrugo A.G.; Millán M.S.
In the field of ophthalmology, retinal images are essential for the diagnosis of many diseases. These images are acquired with a device called the retinal camera. However, often small dust particles in the sensor produce image artifacts that can be confused with small lesions, such as micro-aneurysms. The digital removal of artifacts can be understood as an inpainting process in which a set of pixels are replaced with a value obtained from the surrounding area. In this paper, we propose a methodology based on the sparse representations and dictionary learning for the removal of artifacts in retinal images. We test our method on real retinal images coming from the clinical setting with actual dust artifacts. We compare our restoration results with a diffusion-based inpainting technique. Encouraging experimental results show that our method can successfully remove the artifacts, while assuring the continuity of the retinal structures, like blood vessels. © 2019 IEEE.
On the illumination compensation of retinal images by means of the bidimensional empirical mode decomposition
(SPIE, 2015) Marrugo A.G.; Vargas R.; Chirino M.; Millán M.S.; Garcia-Arteaga J.D.; Brieva J.; Lepore N.
Retinal images are used for diagnostic purposes by ophthalmologists. However, despite controlled conditions in acquisition retinal images often suffer from non-uniform illumination which hinder their clinical use. In this work we propose the compensation of the illumination by modeling the intensity as a sum of non-stationary signals using bidimensional empirical mode decomposition (BEMD). We compare the estimation and compensation of the background illumination with a widely used technique based retinal image pixel classification. The proposed method has shown to provide a better estimation of the background illumination, particularly in complicated areas such as the optic disk (usually bright) and the periphery of fundus images (usually dim). © 2015 SPIE.
Programmable diffractive lens for ophthalmic application
(SPIE, 2014) Millán M.S.; Pérez-Cabré E.; Romero L.A.; Ramírez N.
Pixelated liquid crystal displays have been widely used as spatial light modulators to implement programmable diffractive optical elements, particularly diffractive lenses. Many different applications of such components have been developed in information optics and optical processors that take advantage of their properties of great flexibility, easy and fast refreshment, and multiplexing capability in comparison with equivalent conventional refractive lenses. We explore the application of programmable diffractive lenses displayed on the pixelated screen of a liquid crystal on silicon spatial light modulator to ophthalmic optics. In particular, we consider the use of programmable diffractive lenses for the visual compensation of refractive errors (myopia, hypermetropia, astigmatism) and presbyopia. The principles of compensation are described and sketched using geometrical optics and paraxial ray tracing. For the proof of concept, a series of experiments with artificial eye in optical bench are conducted. We analyze the compensation precision in terms of optical power and compare the results with those obtained by means of conventional ophthalmic lenses. Practical considerations oriented to feasible applications are provided. © 2014 Society of Photo-Optical Instrumentation Engineers.
Programmable diffractive optical elements for extending the depth of focus in ophthalmic optics
(SPIE, 2015) Romero L.A.; Millán M.S.; Jaroszewicz Z.; Kołodziejczyk A.; Romero E.; Lepore N.
The depth of focus (DOF) defines the axial range of high lateral resolution in the image space for object position. Optical devices with a traditional lens system typically have a limited DOF. However, there are applications such as in ophthalmology, which require a large DOF in comparison to a traditional optical system, this is commonly known as extended DOF (EDOF). In this paper we explore Programmable Diffractive Optical Elements (PDOEs), with EDOF, as an alternative solution to visual impairments, especially presbyopia. These DOEs were written onto a reflective liquid cystal on silicon (LCoS) spatial light modulator (SLM). Several designs of the elements are analyzed: the Forward Logarithmic Axicon (FLAX), the Axilens (AXL), the Light sword Optical Element (LSOE), the Peacock Eye Optical Element (PE) and Double Peacock Eye Optical Element (DPE). These elements focus an incident plane wave into a segment of the optical axis. The performances of the PDOEs are compared with those of multifocal lenses. In all cases, we obtained the point spread function and the image of an extended object. The results are presented and discussed. © 2015 SPIE.
Restoration of retinal images with space-variant blur
(2014) Marrugo A.G.; Millán M.S.; Šorel M.; Šroubek F.
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. © 2014 Society of Photo-Optical Instrumentation Engineers.
Robust detection and removal of dust artifacts in retinal images via dictionary learning and sparse-based inpainting
(SPIE, 2019) Sierra E.; Barrios E.; Marrugo A.G.; Millán M.S.; Alam M.S.
Retinal images are acquired with eye fundus cameras which, like any other camera, can suffer from dust particles attached to the sensor and lens. These particles impede light from reaching the sensor, and therefore they appear as dark spots in the image which can be mistaken as small lesions like microaneurysms. We propose a robust method for detecting dust artifacts from more than one image as input and, for the removal, we propose a sparse-based inpainting technique with dictionary learning. The detection is based on a closing operation to remove small dark features. We compute the difference with the original image to highlight the artifacts and perform a filtering approach with a filter bank of artifact models of different sizes. The candidate artifacts are identified via non-maxima suppression. Because the artifacts do not change position in the images, after processing all input images, the candidate artifacts which are not in the same approximate position in different images are rejected and kept unchanged in the image. The experimental results show that our method can successfully detect and remove artifacts, while ensuring the continuity of retinal structures, such as blood vessels. © 2019 SPIE. Downloading of the abstract is permitted for personal use only.