Vessels In Retinal Images Research Articles

On the Adaptive Detection of Blood Vessels in Retinal Images

This paper proposes an automated blood vessel detection scheme based on adaptive contrast enhancement, feature extraction, and tracing. Feature extraction of small blood vessels is performed by using the standard deviation of Gabor filter responses. Tracing of vessels is done via forward detection, bifurcation identification, and backward verification. Tests over twenty images show that for normal images, the true positive rate (TPR) ranges from 80% to 91%, and their corresponding false positive rates (FPR) range from 2.8% to 5.5%. For abnormal images, the TPR ranges from 73.8% to 86.5% and the FPR ranges from 2.1% to 5.3%, respectively. In comparison with two published solution schemes that were also based on the STARE database, our scheme has lower FPR for the reported TPR measure.

MORPHOLOGICAL STRUCTURE RECONSTRUCTION OF RETINAL VESSELS IN FUNDUS IMAGES

Vessels in retinal fundus images are useful in revealing the severity of eye-related diseases. In addition, they can act as landmarks for localizing lesions or the central vision area, and guide laser treatment of neovascularization. In this paper, we propose a two-stage scheme to extract vessels and reconstruct the morphological structure of vessels in retinal images. First, we employ mathematical morphology techniques to highlight large and small vessels with respect to their spatial properties. Different curvature response between vessel and noise patterns allows the use of curvature evaluation to remove enhanced vessel-like noise. A set of linear filters finalize the vessel map. However, the resulting vascular structure is incomplete of some important features in bifurcation points and central reflex. In order to rectify the pitfall, a reconstruction process is performed using dynamic local region growth to recover the morphological structure of vessels. Average performance of our method to extract vessels is 83.7% of TPR(True positive rate) and 3.8% of FPR(False positive rate) for 35 retinal images which include 21 abnormal images.

Wavelength dependence of the apparent diameter of retinal blood vessels

Imaging of retinal blood vessels may assist in the diagnosis and monitoring of diseases such as glaucoma, diabetic retinopathy, and hypertension. However, close examination reveals that the contrast and apparent diameter of vessels are dependent on the wavelength of the illuminating light. In this study multispectral images of large arteries and veins within enucleated swine eyes are obtained with a modified fundus camera by use of intravitreal illumination. The diameters of selected vessels are measured as a function of wavelength by cross-sectional analysis. A fixed scale with spectrally independent dimension is placed above the retina to isolate the chromatic effects of the imaging system and eye. Significant apparent differences between arterial and venous diameters are found, with larger diameters observed at shorter wavelengths. These differences are due primarily to spectral absorption in the cylindrical blood column.

Node-matching-based pattern recognition method for retinal blood vessel images

Human retinal blood vessel images possess certain charac- teristics that are suitable for personal identification and other advanced security applications. We propose an image processing algorithm for matching retinal blood vessel patterns based on the locations of inter- sections of blood vessels. The algorithm includes preprocessing, orien- tation registration, and similarity measurement. Preliminary experimental results show that our method achieves good discrimination among im- ages from different subjects. © 2003 Society of Photo-Optical Instrumentation En- gineers. (DOI: 10.1117/1.1613278)

Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images

In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of interest. In this sense, our approach is regarded as knowledge-guided adaptive thresholding, in contrast to most algorithms known from the literature. We apply our general framework to detect vessels in retinal images. An experimental evaluation demonstrates superior performance over global thresholding and a vessel detection method recently reported in the literature. Due to its simplicity and general nature, our novel approach is expected to be applicable to a variety of other applications.

Adaptive image enhancement for retinal blood vessel segmentation

Retinal blood vessel images are enhanced by removing the non-stationary background, which is adaptively estimated based on local neighbourhood information. The result is a much better segmentation of the blood vessels with a simple algorithm and without the need to obtain a priori illumination knowledge of the imaging system.

Image processing for improved eye-tracking accuracy.

Video cameras provide a simple, noninvasive method for monitoring a subject's eye movements. An important concept is that of the resolution of the system, which is the smallest eye movement that can be reliably detected. While hardware systems are available that estimate direction of gaze in real-time from a video image of the pupil, such systems must limit image processing to attain real-time performance and are limited to a resolution of about 10 arc minutes. Two ways to improve resolution are discussed. The first is to improve the image processing algorithms that are used to derive an estimate. Off-line analysis of the data can improve resolution by at least one order of magnitude for images of the pupil. A second avenue by which to improve resolution is to increase the optical gain of the imaging setup (i.e., the amount of image motion produced by a given eye rotation). Ophthalmoscopic imaging of retinal blood vessels provides increased optical gain and improved immunity to small head movements but requires a highly sensitive camera. The large number of images involved in a typical experiment imposes great demands on the storage, handling, and processing of data. A major bottleneck had been the real-time digitization and storage of large amounts of video imagery, but recent developments in video compression hardware have made this problem tractable at a reasonable cost. Images of both the retina and the pupil can be analyzed successfully using a basic toolbox of image-processing routines (filtering, correlation, thresholding, etc.), which are, for the most part, well suited to implementation on vectorizing supercomputers.

Detection of blood vessels in retinal images using two-dimensional matched filters

Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods.