Biometric identification and diagnosis of disease
The bifurcations and other physical characteristics of the inner retinal vascular network are known to vary among individuals, and these individual variances have been used for biometric identification and for early detection of the onset of disease. The mapping of vascular bifurcations is one of the basic steps in biometric identification. Results of such analyses of retinal blood vessel structure can be evaluated against the ground truth data of vascular bifurcations of retinal fundus images that are obtained from the DRIVE dataset. In addition, the classes of vessels of the DRIVE dataset have also been identified, and an automated method for accurate extraction of these bifurcations is also available. Changes in retinal blood circulation are seen with aging and exposure to air pollution, and may indicate cardiovascular diseases such as hypertension and atherosclerosis. Determining the equivalent width of arterioles and venules near the optic disc is also a widely used technique to identify cardiovascular risks. Function
The retina translates an optical image into neural impulses starting with the patterned excitation of the colour-sensitive pigments of its rods and cones, the retinas photoreceptor cells. The excitation is processed by the neural system and various parts of the brain working in parallel to form a representation of the external environment in the brain. The cones respond to bright light and mediate high-resolution colour vision during daylight illumination (also called photopic vision). The rod responses are saturated at daylight levels and dont contribute to pattern vision.
The centre–surround structures are mathematically equivalent to the edge detection algorithms used by computer programmers to extract or enhance the edges in a digital photograph. Thus, the retina performs operations on the image-representing impulses to enhance the edges of objects within its visual field. For example, in a picture of a dog, a cat and a car, it is the edges of these objects that contain the most information. In order for higher functions in the brain (or in a computer for that matter) to extract and classify objects such as a dog and a cat, the retina is the first step to separating out the various objects within the scene. As an example, the following matrix is at the heart of a computer algorithm that implements edge detection. This matrix is the computer equivalent to the centre–surround structure. In this example, each box (element) within this matrix would be connected to one photoreceptor. The photoreceptor in the centre is the current receptor being processed. The centre photoreceptor is multiplied by the +1 weight factor. The surrounding photoreceptors are the "nearest neighbors" to the centre and are multiplied by the −1/8 value. The sum of all nine of these elements is finally calculated. This summation is repeated for every photoreceptor in the image by shifting left to the end of a row and then down to the next line. The total sum of this matrix is zero, if all the inputs from the nine photoreceptors are of the same value.