Informatics Publishing Limited

C. Samuel ¹, S. Antelin Vijila ²

Affiliations
1 Manonmaniam Sundaranar University, Tirunelveli, IN
2 Department of CSE, Manonmaniam Sundaranar University, Tirunelveli, IN

Abstract

This paper reports on the design and test of an image processing algorithm for the segmentation of the optic disk (OD) in fundus images. This work describes the methods to detect main features of fundus images such as optic disk and optic cup in a retinal image, optic disk (OD) appears as a roughly round region which is brighter than its surrounding. The location of any abnormality in a retinal image relative to OD is useful for later analysis and classification of pathologies. Optic cup is found using their high greylevel variation and their contours are determined by means of morphological techniques. The blood vessels are highlighted using bottom hat transform and morphological dilation after edge detection. All the enhanced features are then combined in the fundus image for the detection of abnormalities in the eye. In this project, we do Segmentation into a piecewise constant structure which requires one more step using optic disc Extraction algorithm (ODE). Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. This scheme has been tested on a number of retinal images in which the optical disc has been successfully segmented.

Keywords

Optic Disc, Retina, Fundus Images, Pathologies.

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M. Sorna Selvi ¹, S. Antelin Vijila ¹, M. Mohammed Ashiqa ¹

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, IN

Abstract

Segmentation is an important problem of Image processing. It has attracted much attention due to its potential value for applications and its theoretical challenges. In this proposed System MAP-ML estimation is used to segment the objects in the images, The Proposed Method contains a Graph cut Algorithm to overcome computation complexity and over segmentation. This is an unsupervised algorithm which automatically segments the given input image into regions according to relevant texture and colors.

Keywords

Graph Cuts, Markov Random Fields, Maximum a Posteriori, Maximum Likelihood.

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