Automated Blood Vessel Segmentation of Retinal Images

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Abstract

Retinal Blood vessel morphology can be an important indicator for many diseases such as diabetes mellitus, hypertension and arteriosclerosis, and the measurement of geometrical changes in retinal veins and arteries and can be applied to a variety of clinical studies. Segmentation of the retinal blood vessels is an assistance to understand more about its morphology and will provide a better source of information for studying the various related diseases. Two of the major problems in the segmentation of retinal blood vessels are the presence of a wide variety of vessel widths and inhomogeneous background of the retina. Computer based analysis for automated segmentation of blood vessels in retinal images will help eye care specialists screen larger populations for vessel abnormalities. We present a method of automated segmentation, comparing both fluorescent and fundus images of the retinal blood vessel. These segmentations are compared against manual measurements and between imaging techniques.

Eyes are organs that detect light. Different kinds of light-sensitive organs are found in a variety of animals. The simplest eyes do nothing but detect whether the surroundings are light or dark, which is sufficient for the entrainment of circadian rhythms but can hardly be called vision. More complex eyes can distinguish shapes and colors. The visual fields of some such complex eyes largely overlap, to allow better depth perception (binocular vision), as in humans; and others are placed so as to minimize the overlap, such as in rabbits and chameleons.

Image Segmentation

Segmentation refers to the grouping of an image into individual entities where an object is distinguished from its surrounding in a scene. It allows a quantitative measurement of the geometrical changes of arteries, tortuosity or lengths and provides the localization of landmark points such as, bifurcations needed for image registration. Therefore, automated vasculature measurement could reduce both the expenditure of resources in terms of specialists and the examination time and provide an objective, precise measurement of retinal blood vessel structure and other pathologies, which motivate the development of a robust vessel segmentation method .

A central feature in such diagnosis is the appearance of blood vessels in retinal images. Segmentation of these vessels enables eye care specialists to screen larger populations for vessel abnormalities. However automated retinal image segmentation is complicated by the fact that the width of retinal images can vary from very large to small, and that the local contrast of vessels is unstable(inhomogenous background). Thresholding defines a region of interest before image segmentation will limit the processing of the defined region so no computing resource is wasted for other irrelevant areas. This also reduces the amount of editing needed after image segmentation because object boundaries are generated within the defined regions.

Defining a region of interest before image segmentation will limit the processing the defined region so no computing resource is wasted for other irrelevant areas. This also reduces the amount of editing needed after image segmentation because object boundaries are generated within the defined regions. Image segmentation by thresholding is a simple but powerful approach for images containing solid objects which are distinguishable from the background or other objects in terms of pixel intensity values. The pixel thresholds are normally adjusted interactively and displayed in real-time on screen. When the values are defined properly, the boundaries are traced for all pixels within the range in the image. Grayscale thresholding works well when an image that has uniform regions and contrasting background. The histogram of the 3-D image is first calculated and an optimal threshold to divide the image into object and background is derived by finding the valley from the histogram.

Objectives

We hope to realize the following objectives through this project:

1. To develop and implement a novel method of automated image segmentation.

2. To develop an algorithm that can differentiate small blood vessels as against the background.

3. To compare segmentation of fluorescent and fundus images by manual and automated methods of segmentation.

4. To quantify and validate the supremacy of automated method over manual thresholding.

Further our aim is to emphasize the significance of our work by putting forward an algorithm which is relatively easy for implementation and hence the viability of a realizable and repeatable step.

Materials

We have put forward an algorithm which segments both fluorescent and fundus images. The algorithm is relatively easy for implementation as it requires no elaborate instruments or materials.

The following are a list of the materials required for our method of segmentation:

1. Retinal Imaging using Fundus Camera

2. Matlab-Image Processing Tool Box

3. Operating System

Conclusion

Retinal Blood vessel morphology is an important indicator for many diseases such as diabetes mellitus, hypertension and arteriosclerosis, and the measurement of geometrical changes in retinal veins and arteries and is applied to a variety of clinical studies. Segmentation of the retinal blood vessels is an assistance to understand more about its morphology and provides a better source of information for studying the various related diseases. Two of the major problems in the segmentation of retinal blood vessels namely the presence of a wide variety of vessel widths and inhomogeneous background of the retina have been addressed. A method of automated segmentation for both fluorescent and fundus images of the retinal blood vessel has been proposed and this segmentation has been compared against manual measurements and the efficiency of the automated algorithm has been quantified.

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