Graph Theory for Computer Science

3Advanced Image Segmentation Using Graph Cut Technique

Ramasubramanian Bhoopalan* and Priyadharshini S.

Department of Electronics and Communication, SRM TRP Engineering College, Tamil Nadu, India

Abstract

Diabetic retinopathy (DR) is a common consequence of diabetes mellitus that is the primary cause of visual impairment in the world. For an efficient diagnosis and therapy monitoring, early detection and precise segmentation of diseased abnormalities in retinal pictures are essential. This abstract describes a new method for advanced image segmentation designed especially for images of DR using the graph cut technique. To accomplish accurate segmentation, the suggested method takes advantage of the inherent features of DR images, including the presence of exudates, hemorrhages, microaneurysms, and other pathological entities. Preprocessing methods are initially used to improve image quality, lower noise, and maintain uniform lighting levels. After that, the image is represented as a graph with pixels acting as nodes and edges encoding spatial relationships and pixel affinities. In order to effectively separate diseased lesions from healthy retinal tissue, the graph is subsequently partitioned into foreground and background sections using the graph cut technique. Graph cut optimally delineates boundaries between various anatomical structures by structuring the segmentation process as an energy minimization problem. This ensures correct localization of diseased characteristics ...