Enhancement of retinal blood vessel segmentation and classification

Image segmentation is a decomposition of scene into its components. It is a key step in image analysis. Edge, point, line, boundary, texture and region detection are the various forms of image segmentation. In human visual Systems, edges are more sensitive than other picture elements. Edge detection technique when used alone for image segmentation results in small gaps in edge Boundaries to any of the neighbouring pixels. A new supervised method for segmentation of blood vessels in retina photographs is implemented in the project. The purpose of this method is to automate the retinal image analysis. Using the retinal image analysis the retinal abnormality can be detected, to diagnose the retinal blood vessel features which is linked to systemic disease. The morphological segmentation is used the cardiovascular and coronary disease in adult life. The algorithm described here is for integrating edges and regions. Firstly, the edge map of image is obtained by using kirsch edge operator. The algorithm is implemented in MATLAB and the result demonstrates that the algorithm is robust, satisfying and work well for images with non-uniform illumination.