An Integrated Approach Using Automatic Seed Generation and Hybrid Classification for the Detection of Red Lesions in Digital Fundus Images

Abstract

In this paper we propose a novel method for automatic detection of microaneurysms (MA) and hemorrhages (HG)grouped as red lesions. Candidate extraction is achieved by automatic seed generation (ASG) which is devoid of morphological top hat transform (MTH). For classification we tested on linear discriminant classifier (LMSE), kNN, GMM, SVM and proposed a Hybrid classifier that incorporates kNN and GMM using 'max' rule. Inclusion of a new feature called elliptic variance during classification phase has significantly reduced the false positives. An integrated approach using ASG and the hybrid classifier reports the best sensitivity of 87% with 95.53% specificity.