Feature Extraction and Quantification in Retinal Fundus Autofluorescence Images of CSR Patients

Retina is the innermost layer of eye, responsible for vision. Central Serous Retinopathy (CSR) is one of the serious diseases which damages the retinal pigment epithelium cells, leading to serious disorders in vision. Further, it leads to deformation and detachment of retinal layers, causing fluid leakage within the retina and loss of vision. The detection and treatment of this disease at an early stage is very essential. In this paper, an algorithm is developed to extract the features of CSR disease from retinal images and quantify the extent of disease over the retina. The blue wave fundus autofluorescence imaging method is adopted and the algorithm is implemented using MATLAB code. Results shows an accurately segmented CSR affected regions of retina along with percentage of affected area.