Iterative Kernel PCA based Classification of Retinal Images for Diabetic Macular Edema

Abstract

Swelling in the macular region of retina which is also known as macular edema, is a complication of the eye often leading to reduced capacity of vision. Diabetic retinopathy is also a severe complication to vision. In this work, iterative kernel based PCA is proposed which is a novel method used for the classification purpose in diseased retinal images. Exudate detection is carried out via a supervised learning approach using the normal fundus images. Feature extraction is introduced to capture the global characteristics of the fundus images and discriminate the normal from diseased images. The performance of the proposed methodology with the conventional PCA is evaluated based on classification accuracy. Experimental results shows the superior nature of iterative kernel based PCA in terms of performance measures.