Improved detection of the central reflex in retinal vessels using a generalized dual-gaussian model and robust hypothesis testing.

This updates an earlier publication by the authors describing a robust framework for detecting vasculature in noisy retinal fundus images. We improved the handling of the "central reflex" phenomenon in which a vessel has a "hollow" appearance. This is particularly pronounced in dual-wavelength images acquired at 570 and 600 nm for retinal oximetry. It is prominent in the 600 nm images that are sensitive to the blood oxygen content. Improved segmentation of these vessels is needed to improve oximetry. We show that the use of a generalized dual-Gaussian model for the vessel intensity profile instead of the Gaussian yields a significant improvement. Our method can account for variations in the strength of the central reflex, the relative contrast, width, orientation, scale, and imaging noise. It also enables the classification of regular and central reflex vessels. The proposed method yielded a sensitivity of 72% compared to 38% by the algorithm of Can et al., and 60% by the robust detection based on a single-Gaussian model. The specificity for the methods were 95%, 97%, and 98%, respectively.

Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features.

This paper presents an automated method to identify arteries and veins in dual-wavelength retinal fundus images recorded at 570 and 600 nm. Dual-wavelength imaging provides both structural and functional features that can be exploited for identification. The processing begins with automated tracing of the vessels from the 570-nm image. The 600-nm image is registered to this image, and structural and functional features are computed for each vessel segment. We use the relative strength of the vessel central reflex as the structural feature. The central reflex phenomenon, caused by light reflection from vessel surfaces that are parallel to the incident light, is especially pronounced at longer wavelengths for arteries compared to veins. We use a dual-Gaussian to model the cross-sectional intensity profile of vessels. The model parameters are estimated using a robust M-estimator, and the relative strength of the central reflex is computed from these parameters. The functional feature exploits the fact that arterial blood is more oxygenated relative to that in veins. This motivates use of the ratio of the vessel optical densities (ODs) from images at oxygen-sensitive and oxygen-insensitive wavelengths (ODR = OD600/OD570) as a functional indicator. Finally, the structural and functional features are combined in a classifier to identify the type of the vessel. We experimented with four different classifiers and the best result was given by a support vector machine (SVM) classifier. With the SVM classifier, the proposed algorithm achieved true positive rates of 97% for the arteries and 90% for the veins, when applied to a set of 251 vessel segments obtained from 25 dual wavelength images. The ability to identify the vessel type is useful in applications such as automated retinal vessel oximetry and automated analysis of vascular changes without manual intervention.

Integrated analysis of vascular and nonvascular changes from color retinal fundus image sequences.

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.

Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy.

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.

Automated change analysis from fluorescein angiograms for monitoring wet macular degeneration.

Detection and analysis of changes from retinal images is important in clinical practice, quantitative scoring of clinical trials, computer-assisted reading centers, and in medical research. This paper presents a fully-automated approach for robust detection and classification of changes in longitudinal time-series of fluorescein angiograms (FA). The changes of interest here are related to the development of choroidal neo-vascularization (CNV) in wet macular degeneration. Specifically, the changes in CNV regions as well as the retinal pigment epithelium (RPE) hypertrophic regions are detected and analyzed to study the progression of disease and effect of treatment. Retinal features including the vasculature, vessel branching/crossover locations, optic disk and location of the fovea are first segmented automatically. The images are then registered to sub-pixel accuracy using a 12-dimensional mapping that accounts for the unknown retinal curvature and camera parameters. Spatial variations in illumination are removed using a surface fitting algorithm that exploits the segmentations of the various features. The changes are identified in the regions of interest and a Bayesian classifier is used to classify the changes into clinically significant classes. The automated change analysis algorithms were found to have a success rate of 83%

Algorithms for automated oximetry along the retinal vascular tree from dual-wavelength fundus images.

We present an automated method to perform accurate, rapid, and objective measurement of the blood oxygen saturation over each segment of the retinal vascular hierarchy from dual-wavelength fundus images. Its speed and automation (2 s per entire image versus 20 s per segment for manual methods) enables detailed level-by-level measurements over wider areas. An automated tracing algorithm is used to estimate vessel centerlines, thickness, directions, and locations of landmarks such as bifurcations and crossover points. The hierarchical structure of the vascular network is recovered from the trace fragments and landmarks by a novel algorithm. Optical densities (OD) are measured from vascular segments using the minimum reflected intensities inside and outside the vessel. The OD ratio (ODR=OD600/OD570) bears an inverse relationship to systemic HbO2 saturation (SO2). The sensitivity for detecting saturation change when breathing air versus pure oxygen was calculated from the measurements made on six subjects and was found to be 0.0226 ODR units, which is in good agreement with previous manual measurements by the dual-wavelength technique, indicating the validity of the automation. A fully automated system for retinal vessel oximetry would prove useful to achieve early assessments of risk for progression of disease conditions associated with oxygen utilization.

Robust model-based vasculature detection in noisy biomedical images.

This paper presents a set of algorithms for robust detection of vasculature in noisy retinal video images. Three methods are studied for effective handling of outliers. The first method is based on Huber's censored likelihood ratio test. The second is based on the use of a alpha-trimmed test statistic. The third is based on robust model selection algorithms. All of these algorithms rely on a mathematical model for the vasculature that accounts for the expected variations in intensity/texture profile, width, orientation, scale, and imaging noise. These unknown parameters are estimated implicitly within a robust detection and estimation framework. The proposed algorithms are also useful as nonlinear vessel enhancement filters. The proposed algorithms were evaluated over carefully constructed phantom images, where the ground truth is known a priori, as well as clinically recorded images for which the ground truth was manually compiled. A comparative evaluation of the proposed approaches is presented. Collectively, these methods outperformed prior approaches based on Chaudhuri et al. (1989) matched filtering, as well as the verification methods used by prior exploratory tracing algorithms, such as the work of Can et aL (1999). The Huber censored likelihood test yielded the best overall improvement, with a 145.7% improvement over the exploratory tracing algorithm, and a 43.7% improvement in detection rates over the matched filter.