Atp-Mlsm: Angular Texture Pattern-Multi-Level Set Model-Based Retinal Image Segmentation Approach

This paper proposes an Angular Texture Pattern (ATP)-Multi-Level Set Model (MLSM)-based retinal image segmentation approach. The location of Optical Disk (OD) is estimated by initially collecting the blood vessel region from the retinal image. Based on the identification of OD location, the bright pixel values are estimated to provide the boundary detail of OD. From this boundary detail, the Region of Interest (ROI) such as Hard Exudates (HE) is obtained in the binary form, to enable contour formation for the OD and HE. Then, the cup-to-disk ratio of the OD is calculated, and the number of HEs is counted. The severity level of the DR and Glaucoma is determined based on the cup-to-disk ratio of the OD and HE count value. The proposed algorithm is tested by using the retinal images of the DIARETDB1 and MESSIDOR database. The proposed approach achieves better performance than the existing OD segmentation methodologies.