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An algorithm for the real-time registration of a retinal video sequence captured with a scanning digital ophthal moscope (SDO) to a retinal composite image is presented. This method is designed for a computer-assisted retinal laser photocoagulation system to compensate for retinal motion and hence enhance the accuracy, speed, and patient safety of retinal laser treatments. The procedure combines intensity and feature-based registration techniques. For the registration of an individual frame, the translational frame-to-frame motion between preceding and current frame is detected by normalized cross correlation. Next, vessel points on the current video frame are identified and an initial transformation estimate is constructed from the calculated translation vector and the quadratic registration matrix of the previous frame. The vessel points are then iteratively matched to the segmented vessel centerline of the composite image to refine the initial transformation and register the video frame to the composite image. Criteria for image quality and algorithm convergence are introduced, which assess the exclusion of single frames from the registration process and enable a loss of tracking signal if necessary. The algorithm was successfully applied to ten different video sequences recorded from patients. It revealed an average accuracy of 2.47 \pm 2.0 pixels (\sim23.2 \pm 18.8 \mu \m) for 2764 evaluated video frames and demonstrated that it meets the clinical requirements. [ABSTRACT FROM AUTHOR]

Real-time spatial referencing is an important alternative to tracking for designing spatially aware ophthalmic instrumentation for procedures such as laser photocoagulation and perimetry. It requires independent, fast registration of each image frame from a digital video stream (1024 × 1024 pixels) to a spatial map of the retina. Recently, we have introduced a spatial referencing algorithm that works in three primary steps: 1) tracing the retinal vasculature to extract image feature (landmarks); 2) invariant indexing to generate hypothesized landmark correspondences and initial transformations; and 3) alignment and verification steps to robustly estimate a 12-parameter quadratic spatial transformation between the image frame and the map. The goal of this paper is to introduce techniques to minimize the amount of computation for successful spatial referencing. The fundamental driving idea is to make feature extraction subservient to registration and, therefore, only produce the information needed for verified, accurate transformations. To this end, the image is analyzed along one-dimensional, vertical and horizontal grid lines to produce a regular sampling of the vasculature, needed for step 3) and to initiate step 1). Tracing of the vascular is then prioritized hierarchically to quickly extract landmarks and groups (constellations) of landmarks for indexing. Finally, the tracing and spatial referencing computations are integrated so that landmark constellations found by tracing are tested immediately. The resulting implementation is an order-of-magnitude faster with the same success rate. The average total computation time is 31.2 ms per image on a 2.2-GHz Pentium Xeon processor. [ABSTRACT FROM AUTHOR]

The vascular tree of the retina is likely the most representative and stable feature for eye fundus images in registration. Based on the reconstructed vascular tree, we propose an elastic matching algorithm to register pairs of fundus images. The identified vessels are thinned and approximated using short line segments of equal length that results a set of elements. The set of elements corresponding to one vascular tree are elastically deformed to optimally match the set of elements of another vascular tree, with the guide of an energy function to finally establish pixel relation- ship between both vascular trees. The mapped positions of pixels in the transformed retinal image are computed to be the sum of their original locations and corresponding displacement vectors. For the purpose of performance comparison, a weak affine model based fast chamfer matching technique is proposed and implemented. Experiment results validated the effectiveness of the elastic matching algorithm and its advantage over the weak affine model for registration of retinal fundus images. [ABSTRACT FROM AUTHOR]

Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a postprocessing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy. [ABSTRACT FROM PUBLISHER]

Motivated by the goals of automatically extracting vessel segments and constructing retinal vascular trees with anatomical realism, this paper presents and analyses an algorithm that combines vessel segmentation and grouping of the extracted vessel segments. The proposed method aims to restore the topology of the vascular trees with anatomical realism for clinical studies and diagnosis of retinal vascular diseases, which manifest abnormalities in either venous and/or arterial vascular systems. Vessel segments are grouped using extended Kalman filter which takes into account continuities in curvature, width, and intensity changes at the bifurcation or crossover point. At a junction, the proposed method applies the minimum-cost matching algorithm to resolve the conflict in grouping due to error in tracing. The system was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images. The dataset contained a mixture of normal and pathological images. In addition, six pathological fluorescein angiogram sequences were also included in this study. The results were compared against the groundtruth images provided by a physician, achieving average success rates of 88.79% and 90.09%, respectively. [ABSTRACT FROM PUBLISHER]

In this paper, we present an algorithm to detect the presence of diabetic retinopathy (DR)-related lesions from fundus images based on a common analytical approach that is capable of identifying both red and bright lesions without requiring specific pre- or postprocessing. Our solution constructs a visual word dictionary representing points of interest (PoIs) located within regions marked by specialists that contain lesions associated with DR and classifies the fundus images based on the presence or absence of these PoIs as normal or DR-related pathology. The novelty of our approach is in locating DR lesions in the optic fundus images using visual words that combines feature information contained within the images in a framework easily extendible to different types of retinal lesions or pathologies and builds a specific projection space for each class of interest (e.g., white lesions such as exudates or normal regions) instead of a common dictionary for all classes. The visual words dictionary was applied to classifying bright and red lesions with classical cross validation and cross dataset validation to indicate the robustness of this approach. We obtained an area under the curve (AUC) of 95.3% for white lesion detection and an AUC of 93.3% for red lesion detection using fivefold cross validation and our own data consisting of 687 images of normal retinae, 245 images with bright lesions, 191 with red lesions, and 109 with signs of both bright and red lesions. For cross dataset analysis, the visual dictionary also achieves compelling results using our images as the training set and the RetiDB and Messidor images as test sets. In this case, the image classification resulted in an AUC of 88.1% when classifying the RetiDB dataset and in an AUC of 89.3% when classifying the Messidor dataset, both cases for bright lesion detection. The results indicate the potential for training with different acquisition images under different setup conditions with a high accuracy of referral based on the presence of either red or bright lesions or both. The robustness of the visual dictionary against image quality (blurring), resolution, and retinal background, makes it a strong candidate for DR screening of large, diverse communities with varying cameras and settings and levels of expertise for image capture. [ABSTRACT FROM PUBLISHER]

Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy (DR) grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 \pm 0.01 is achieved in a “DR/non-DR”-type classification based on the presence or absence of the microaneurysms. [ABSTRACT FROM PUBLISHER]

Future retinal therapies will be partially automated in order to increase the positioning accuracy of surgical tools. Proposed untethered microrobotic approaches that achieve this increased accuracy require localization information for their control. Since the environment of the human eye is externally observable, images can be used to localize the microrobots. In this paper, the common methods of ophthalmoscopy assuming a single stationary camera are examined and compared with respect to their imaging and localizing properties on a schematic model of the human eye. The first algorithm for wide-angle intraocular localization based on indirect ophthalmoscopy is presented, and its sensitivity with respect to uncertainties in the parameters of individual eyes is estimated. A calibration technique to account for these uncertainties is proposed, and the localization algorithm is validated with experiments in a model eye. [ABSTRACT FROM AUTHOR]

Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists. [ABSTRACT FROM AUTHOR]

High-speed videokeratoscopy is an emerging technique that enables study of the corneal surface and tear-film dynamics. Unlike its static predecessor, this new technique results in a very large amount of digital data for which storage needs be- come significant. We aimed to design a compression technique that would use mathematical functions to parsimoniously fit corneal surface data with a minimum number of coefficients. Since the Zernike polynomial functions that have been traditionally used for modeling corneal surfaces may not necessarily correctly represent given corneal surface data in terms of its optical performance, we introduced the concept of Zernike polynomial-based rational functions. Modeling optimality criteria were employed in terms of both the rms surface error as well as the point spread function cross- correlation. The parameters of approximations were estimated using a nonlinear least-squares procedure based on the Levenberg- Marquardt algorithm. A large number of retrospective videoker- atoscopic measurements were used to evaluate the performance of the proposed rational-function-based modeling approach. The results indicate that the rational functions almost always outperform the traditional Zernike polynomial approximations with the same number of coefficients. [ABSTRACT FROM AUTHOR]

Algorithms are presented for integrated analysis of both vascular and nonvascular changes observed in longitudinal time-series of color retinal fundus images, extending our prior work. A Bayesian model selection algorithm that combines color change information, and image understanding systems outputs in a novel manner is used to analyze vascular changes such as increase/decrease in width, and disappearance/appearance of vessels, as well as nonvascular changes such as appearance/disappearance of different kinds of lesions. The overall system is robust to false changes due to inter-image and intra-image nonuniform illumination, imaging artifacts such as dust particles in the optical path, alignment errors and outliers in the training-data. An expert observer validated the algorithms on 54 regions selected from 34 image pairs. The regions were selected such that they represented diverse types of vascular changes of interest, as well as no-change regions. The algorithm achieved a sensitivity of 82% and a 9% false positive rate for vascular changes. For the nonvascular changes, 97% sensitivity and a 10% false positive rate are achieved. The combined system is intended for diverse applications including computer-assisted retinal screening, image-reading centers, quantitative monitoring of disease onset and progression, assessment of treatment efficacy, and scoring clinical trials. [ABSTRACT FROM AUTHOR]

This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) < 3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM). [ABSTRACT FROM AUTHOR]

A fully automated approach is presented for robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy. The method is robust to: 1) spatial variations in illumination resulting from instrument limitations and changes both within, and between patient visits; 2) imaging artifacts such as dust particles; 3) outliers in the training data; 4) segmentation and alignment errors. Robustness to illumination variation is achieved by a novel iterative algorithm to estimate the reflectance of the retina exploiting automatically extracted segmentations of the retinal vasculature, optic disk, fovea, and pathologies. Robustness to dust artifacts is achieved by exploiting their spectral characteristics, enabling application to film-based, as well as digital imaging systems. False changes from alignment errors are minimized by subpixel accuracy registration using a 12-parameter transformation that accounts for unknown retinal curvature and camera parameters. Bayesian detection and classification algorithms are used to generate a color-coded output that is readily inspected. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 97% change detection rate, a 3% miss rate, and a 10% false alarm rate. The performance in correctly classifying the changes was 99.3%. A self-consistency metric, and an error factor were developed to measure performance over more than two periods. The average self consistency was 94% and the error factor was 0.06%. Although this study focuses on diabetic changes, the proposed techniques have broader applicability in ophthalmology. [ABSTRACT FROM AUTHOR]

Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG. The 12-lead ECGs of 30 patients in atrial fibrillation were analyzed. Atrial activity was extracted by three algorithms, Spatiotemporal QRST cancellation (STC), principal component analysis (PCA), and independent component analysis (ICA). The amplitude and frequency characteristics of the extracted atrial signals were compared between algorithms and against reference data. Mean (standard deviation) amplitude of QRST segments of VI was 0.99 (0.54) mV, compared to 0.18 (0.11) mV (STC), 0.19 (0.13) mV (PCA), and 0.29 (0.22) mV (ICA). Hence, for all algorithms there were significant reductions in the amplitude of the ventricular activity compared with that in V1. Reference atrial signal amplitude in V1 was 0.18 (0.11) mV, compared to 0.17 (0.10) mV (STC), 0.12 (0.09) mV (PCA), and 0.18 (0.13) mV (ICA) in the extracted atrial signals. PCA tended to attenuate the atrial signal in these segments. There were no significant differences for any of the algorithms when comparing the amplitude of the reference atrial signal with that of the extracted atrial signals in segments in which ventricular activity had been removed. There were no significant differences between algorithms in the frequency characteristics of the extracted atrial signals. There were discrepancies in amplitude and frequency characteristics of the atrial signal in only a few cases resulting from notable residual ventricular activity for PCA and ICA algorithms. In conclusion, the extracted atrial signals from these algorithms exhibit very similar amplitude and frequency characteristics. Users of these algorithms should be observant of residual ventricular activities which can affect the analysis of the fibrillatory waveform in clinical practice. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM PUBLISHER]