Search

Handheld scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) systems facilitate imaging of young children and subjects that have difficulty fixating. More compact and lightweight probes allow for better portability and increased comfort for the operator of the handheld probe. We describe a very compact, novel SLO and OCT handheld probe design. A single 2D microelectromechanical systems (MEMS) scanner and a custom optical design using a converging beam prior to the scanner permitted significant reduction in the system size. Our design utilized a combination of commercial and custom optics that were optimized in Zemax to achieve near diffraction-limited resolution of 8 μm over a 7° field of view. The handheld probe has a form factor of 7 x 6 x 2.5 cm and a weight of only 94 g, which is over an order of magnitude lighter than prior SLO-OCT handheld probes. Images were acquired from a normal subject with an incident power on the eye under the ANSI limit. With this device, which is the world’s lightest and smallest SLO-OCT system, we were able to visualize parafoveal cone photoreceptors and nerve fiber bundles without the use of adaptive optics.

We describe an efficient approach for the automated segmentation of pathological/morphological structures in ophthalmic Spectral Domain Optical Coherence Tomography (SDOCT) images. In this algorithm, image pixels are treated as nodes of a graph with edge weights assigned to associate pairs of pixels. The weights vary according to the distances, brightness differences, and feature variations between pixel pairs. Cuts through the graph with minimum accumulated weights correspond to morphological layer boundaries. This approach has been applied to SDOCT images with encouraging results and thus forms an adaptable framework for the segmentation of many different ophthalmic structures.

Accurate detection-characterization of drusen is an important imaging biomarker of age-related macular degeneration (AMD) progression. We report on the development of an automatic method for detection and segmentation of drusen in retinal images captured via high speed spectral domain coherence tomography (SDOCT) systems. The proposed algorithm takes advantage of a priori knowledge about the retina shape and structure in the AMD and normal eyes. In the first step, the location of the retinal nerve fiber layer (RNFL) is estimated by searching for the locally connected segments with high radiometric vertical gradients appearing in the upper section of the SDOCT scans. The high reflective and locally connected pixels that are spatially located below the RNFL layer are taken as the initial estimate of the retinal pigment epithelium (RPE) layer location. Such rough estimates are smoothed and improved by using a slightly modified implementation of the Xu-Prince gradient vector flow based deformable snake method. Further steps, including a two-pass scan of the image, remove outliers and improve the accuracy of the estimates. Unlike healthy eyes commonly exhibiting a convex RPE shape, the shape of the RPE layer in AMD eyes might include abnormalities due to the presence of drusen. Therefore by enforcing local convexity condition and fitting second or fourth order polynomials to the possibly unhealthy (abnormal) RPE curve, the health (normal) shape of the RPE layer is estimated. The area between the estimated normal and the segmented RPE outlines is marked as possible drusen location. Moreover, fine-tuning steps are incorporated to improve the accuracy of the proposed technique. All methods are implemented in a graphical user interface (GUI) software package based on MATLAB platform. Minor errors in estimating drusen volume can be easily manually corrected using the user-friendly software interface and the program is constantly refined to correct for the repeating errors. This semi-supervised approach significantly reduces the time and resources needed to conduct a large-scale AMD study. The computational complexity of the core automated segmentation technique is attractive as it only takes about 6.5 seconds on a conventional PC to segment, display, and record drusen locations in an image of size (512 × 1000) pixels. Experimental results on segmenting drusen in SDOCT images of different subjects are included, which attest to the effectiveness of the proposed technique.

Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues related to designing a practical SR system, namely reconstruction accuracy and computational efficiency. Reconstruction accuracy refers to the problem of designing a robust SR method applicable to images from different imaging systems. We study a general framework for optimal reconstruction of images from grayscale, color, or color filtered (CFA) cameras. The performance of our proposed method is boosted by using powerful priors and is robust to both measurement (e.g. CCD read out noise) and system noise (e.g. motion estimation error). Noting that the motion estimation is often considered a bottleneck in terms of SR performance, we introduce the concept of "constrained motions" for enhancing the quality of super-resolved images. We show that using such constraints will enhance the quality of the motion estimation and therefore results in more accurate reconstruction of the HR images. We also justify some practical assumptions that greatly reduce the computational complexity and memory requirements of the proposed methods. We use efficient approximation of the Kalman Filter (KF) and adopt a dynamic point of view to the SR problem. Novel methods for addressing these issues are accompanied by experimental results on real data.

In the last two decades a variety of super-resolution (SR) methods have been proposed. These methods usually address the problem of fusing a set of monochromatic images to produce a single monochromatic image with higher spatial resolution. In this paper we address the dynamic and color SR problems of reconstructing a high-quality set of colored super-resolved images from low-quality mosaiced frames. Our approach includes a hybrid method for simultaneous SR and demosaicing, this way taking into account practical color measurements encountered in video sequences. For the case of translational motion and common space-invariant blur, the proposed method is based on a very fast and memory efficient approximation of the Kalman filter. Experimental results on both simulated and real data are supplied, demonstrating the presented algorithm, and its strength.

In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial resolution and color filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteriori (MAP) estimation technique by minimizing a multi-term cost function. The L1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance component. Finally, an additional regularization term is used to force similar edge orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.