The detection and quantification of retinopathy using digital angiograms

Measurement of retinal vessel widths from fundus images based on 2-D modeling

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.

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.

Median-based robust algorithms for tracing neurons from noisy confocal microscope images

This paper presents a method to exploit rank statistics to improve fully automatic tracing of neurons from noisy digital confocal microscope images. Previously proposed exploratory tracing (vectorization) algorithms work by recursively following the neuronal topology, guided by responses of multiple directional correlation kernels. These algorithms were found to fail when the data was of lower quality (noisier, less contrast, weak signal, or more discontinuous structures). This type of data is commonly encountered in the study of neuronal growth on microfabricated surfaces. We show that by partitioning the correlation kernels in the tracing algorithm into multiple subkernels, and using the median of their responses as the guiding criterion improves the tracing precision from 41% to 89% for low-quality data, with a 5% improvement in recall. Improved handling was observed for artifacts such as discontinuities and/or hollowness of structures. The new algorithms require slightly higher amounts of computation, but are still acceptably fast, typically consuming less than 2 seconds on a personal computer (Pentium III, 500 MHz, 128 MB). They produce labeling for all somas present in the field, and a graph-theoretic representation of all dendritic/axonal structures that can be edited. Topological and size measurements such as area, length, and tortuosity are derived readily. The efficiency, accuracy, and fully-automated nature of the proposed method makes it attractive for large-scale applications such as high-throughput assays in the pharmaceutical industry, and study of neuron growth on nano/micro-fabricated structures. A careful quantitative validation of the proposed algorithms is provided against manually derived tracing, using a performance measure that combines the precision and recall metrics.

Segmentation of vessels from mammograms using a deformable model

Vessel extraction is a fundamental step in certain medical imaging applications such as angiograms. Different methods are available to segment vessels in medical images, but they are not fully automated (initial vessel points are required) or they are very sensitive to noise in the image. Unfortunately, the presence of noise, the variability of the background, and the low and varying contrast of vessels in many imaging modalities such as mammograms, makes it quite difficult to obtain reliable fully automatic or even semi-automatic vessel detection procedures. In this paper a fully automatic algorithm for the extraction of vessels in noisy medical images is presented and validated for mammograms. The main issue in this research is the negative influence of noise on segmentation algorithms. A two-stage procedure was designed for noise reduction. First, a global approach phase including edge detection and thresholding is applied. Then, the local approach phase performs vessel segmentation using a deformable model with a new energy term that reduces the noise still remaining in the image from the first stage. Experimental results on mammograms show that this method has an excellent performance level in terms of accuracy, sensitivity, and specificity. The computation time also makes it suitable for real-time applications within a clinical environment.

Measuring tortuosity of the intracerebral vasculature from MRA images

The clinical recognition of abnormal vascular tortuosity, or excessive bending, twisting, and winding, is important to the diagnosis of many diseases. Automated detection and quantitation of abnormal vascular tortuosity from three-dimensional (3-D) medical image data would, therefore, be of value. However, previous research has centered primarily upon two-dimensional (2-D) analysis of the special subset of vessels whose paths are normally close to straight. This report provides the first 3-D tortuosity analysis of clusters of vessels within the normally tortuous intracerebral circulation. We define three different clinical patterns of abnormal tortuosity. We extend into 3-D two tortuosity metrics previously reported as useful in analyzing 2-D images and describe a new metric that incorporates counts of minima of total curvature. We extract vessels from MRA data, map corresponding anatomical regions between sets of normal patients and patients with known pathology, and evaluate the three tortuosity metrics for ability to detect each type of abnormality within the region of interest. We conclude that the new tortuosity metric appears to be the most effective in detecting several types of abnormalities. However, one of the other metrics, based on a sum of curvature magnitudes, may be more effective in recognizing tightly coiled, "corkscrew" vessels associated with malignant tumors.

Retinal vascular tree morphology: a semi-automatic quantification

A semi-automatic method to measure and quantify geometrical and topological properties of continuous vascular trees in clinical fundus images is described. Measurements are made from binary images obtained with a previously described segmentation process. The skeletons of the segmented trees are produced by thinning,ff branch and crossing points are identified and segments of the trees are labeled and stored as a chain code. The operator selects a tree to be measured and decides if it is an arterial or venous tree. An automatic process then measures the lengths, areas and angles of the individual segments of the tree. Geometrical data and the connectivity information between branches from continuous retinal vessel trees are tabulated. A number of geometrical properties and topological indexes are derived. Vessel diameters and branching angles are validated against manual measurements and several derived geometrical and topological properties are extracted from red-free fundus images of ten normotensive and ten age- and sex-matched hypertensive subjects and compared with previously reported results.

Rapid automated three-dimensional tracing of neurons from confocal image stacks

Algorithms are presented for fully automatic three-dimensional (3-D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxel-based skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of 4 x N2 directional kernels (e.g., N = 32), guided by a generalized 3-D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3-D space. Since the centerlines are of primary interest, the 3-D extension can be accomplished by four rather than six sets of kernels. Additional modifications, such as dynamic adaptation of the correlation kernels, and adaptive step size estimation, were introduced for achieving robustness to photon noise, varying contrast, and apparent discontinuity and/or hollowness of structures. The end product is a labeling of all somas present, graph-theoretic representations of all dendritic/axonal structures, and image statistics such as soma volume and centroid, soma interconnectivity, the longest branch, and lengths of all graph branches originating from a soma. This method is able to work directly with unprocessed confocal images, without expensive deconvolution or other preprocessing. It is much faster that skeletonization, typically consuming less than a minute to trace a 70-MB image on a 500-MHz computer. These properties make it attractive for large-scale automated tissue studies that require rapid on-line image analysis, such as high-throughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project.

Optimal green (red-free) digital imaging of conjunctival vasculature

AIMS/BACKGROUND

Green illumination is commonly used to image vessels of the retina and conjunctiva. The purpose was to derive the best optical set-up for imaging vessels of the conjunctiva.

METHODS

The concept of exposure density was used to predict a digital camera response to imaging vessels on a scleral background. Practical verification was performed to verify vessel contrast because of the difficulties in measuring the spectral components of the imaging system, such as the spectral reflectivity of vessels and sclera. Images of the same conjunctiva were repetitively taken through different coloured filters, using the Nikon FS-2 photo slit-lamp and recorded on different coloured channels of the Kodak DCS 100 digital camera. Gaussian blurred tubular models were fitted to densitometric profiles across three vessels from each image, allowing vessel contrast and width to be objectively measured. These measures were compared using different optical set-ups.

RESULTS

Optimal exposure density calculations and vessel contrast was obtained with the xenon light source filtered with Wratten 99 (green) and Wratten 96 (neutral density, 0.2 log units) gelatine absorption filters using the green channel of the digital camera. This image set-up was associated with a 46% (99% CI 43-51%) to 64% (99% CI 58-72%) increase in contrast compared with vessels imaged without filtration, using the combined colour channel of the digital camera. Although differences in vessel widths resulted, absolute differences were marginal.

CONCLUSION

With the increased use of digital imaging, and the need for image processing of vascular networks, image optimisation is beneficial. This study verified the optimal set-up for non-invasively imaging vessels of the bulbar conjunctiva.

Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter

In this paper, the fitness of estimating vessel profiles with Gaussian function is evaluated and an amplitude-modified second-order Gaussian filter is proposed for the detection and measurement of vessels. Mathematical analysis is given and supported by a simulation and experiments to demonstrate that the vessel width can be measured in linear relationship with the "spreading factor" of the matched filter when the magnitude coefficient of the filter is suitably assigned. The absolute value of vessel diameter can be determined simply by using a precalibrated line, which is typically required since images are always system dependent. The experiment shows that the inclusion of the width measurement in the detection process can improve the performance of matched filter and result in a significant increase in success rate of detection.

Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy

Patients with diabetes require annual screening for effective timing of sight-saving treatment. However, the lack of screening and the shortage of ophthalmologists limit the ocular health care available. This is stimulating research into automated analysis of the reflectance images of the ocular fundus. Publications applicable to the automated screening of diabetic retinopathy are summarised. The review has been structured to mimic some of the processes that an ophthalmologist performs when examining the retina. Thus image processing tasks, such as vessel and lesion location, are reviewed before any intelligent or automated systems. Most research has been undertaken in identification of the retinal vasculature and analysis of early pathological changes. Progress has been made in the identification of the retinal vasculature and the more common pathological features, such as small aneurysms and exudates. Ancillary research into image preprocessing has also been identified. In summary, the advent of digital data sets has made image analysis more accessible, although questions regarding the assessment of individual algorithms and whole systems are only just being addressed.

Quantification and characterisation of arteries in retinal images

A computerised system is presented for the automatic quantification of blood vessel topography in retinal images. This system utilises digital image processing techniques to provide more reliable and comprehensive information for the retinal vascular network. It applies strategies and algorithms for measuring vascular trees and includes methods for locating the centre of a bifurcation, detecting vessel branches, estimating vessel diameter, and calculating angular geometry at a bifurcation. The performance of the system is studied by comparison with manual measurements and by comparing measurements between red-free images and fluorescein images. In general an acceptable degree of accuracy and precision was seen for all measurements, although the system had difficulty dealing with very noisy images and small or especially tortuous blood vessels.

Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms

Algorithms are presented for rapid, automatic, robust, adaptive, and accurate tracing of retinal vasculature and analysis of intersections and crossovers. This method improves upon prior work in several ways: 1) automatic adaptation from frame to frame without manual initialization/adjustment, with few tunable parameters; 2) robust operation on image sequences exhibiting natural variability, poor and varying imaging conditions, including over/under-exposure, low contrast, and artifacts such as glare; 3) does not require the vasculature to be connected, so it can handle partial views; and 4) operation is efficient enough for use on unspecialized hardware, and amenable to deadline-driven computing, being able to produce a rapidly and monotonically improving sequence of usable partial results. Increased computation can be traded for superior tracing performance. Its efficiency comes from direct processing on gray-level data without any preprocessing, and from processing only a minimally necessary fraction of pixels in an exploratory manner, avoiding low-level image-wide operations such as thresholding, edge detection, and morphological processing. These properties make the algorithm suited to real-time, on-line (live) processing and is being applied to computer-assisted laser retinal surgery.

Measurement and classification of retinal vascular tortuosity

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity. Finally, we discuss how the accuracy of these measures can be influence by the method used to extract the blood vessel segments.

The preprocessing of retinal images for the detection of fluorescein leakage

Images of the human retina are routinely used in clinical practice for the diagnosis and management of eye disease. Increased permeability of retinal blood vessels, which is a clinically significant feature, can be visualized with a process known as fluorescein angiography as leakage of fluorescence dye into the surrounding tissues. Analyses of such images can be quantified but significant degradation of images due to uneven illumination or occluded optical pathways is often incurred during image capture. We describe a procedure to restore fluorescein angiographic retinal images so that quantitative computation can be reliably performed. Analysis of the image acquisition system reveals that captured images are composed of two functions, one describing the true underlying image and the other the degradation incurred. These two functions are independent of one another and it is possible to estimate the degradation from an isolated captured image and restore it appropriately. Any leakage of fluorescein dye is then detected by analysing the restored angiographic sequence over time and finding areas of the image that do not have the usual decrease in fluorescence intensity.

A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering

In this paper we present a new unsupervised fuzzy algorithm for vessel tracking that is applied to the detection of the ocular fundus vessels. The proposed method overcomes the problems of initialization and vessel profile modeling that are encountered in the literature and automatically tracks fundus vessels using linguistic descriptions like "vessel" and "nonvessel." The main tool for determining vessel and nonvessel regions along a vessel profile is the fuzzy C-means clustering algorithm that is fed with properly preprocessed data. Additional procedures for checking the validity of the detected vessels and handling junctions and forks are also presented. The application of the proposed algorithm to fundus images and simulated vessels resulted in very good overall performance and consistent estimation of vessel parameters.

X-ray fluoroscopy spatio-temporal filtering with object detection

One potential way to reduce patient and staff X-ray fluoroscopy dose is to reduce the quantum exposure to the detector and compensate the additional noise with digital filtering. A new filtering method, spatio-temporal filtering with object detection, is described that reduces noise while minimizing motion and spatial blur. As compared to some conventional motion-detection filtering schemes, this object-detection method incorporates additional a priori knowledge of image content; i.e. much of the motion occurs in isolated long thin objects (catheters, guide wires, etc.). We create object-likelihood images and use these to control spatial and recursive temporal filtering such as to reduce blurring the objects of interest. We use automatically computed receiver operating characteristic (ROC) curves to optimize the object-likelihood enhancement method and determine that oriented matched filter kernels with 4 orientations are appropriate. The matched filter kernels are simple projected cylinders. We demonstrate the method on several representative X-ray fluoroscopy sequences to which noise is added to simulate very low dose acquisitions. With processing, we find that noise variance is significantly reduced with slightly less noise reduction near moving objects. We estimate an effective exposure reduction greater than 80%