BAT optimization based Retinal artery vein classification

Abstract

The investigation of artery vein changes over time is considered to be the significant diagnosis process of retinal diseases like diabetic retinopathy. The diagnosis includes the characteristics analysis of artery vein vessels, changes in its tortuosity level and artery vein ratio; hence, it is important to classify the artery and vein in a better way. Computer-aided diagnosis requires the automated classification of retinal artery and vein for diagnosing the progression of diseases. In this paper, a supervised classification with Bat algorithm is proposed to discriminate the artery and vein vessels in the retinal fundus images. A novel feature vector space, including both additive colour space as well as luminous chromaticity model colour space, is constructed. BAT algorithm is applied to select the feature group which improve the classification accuracy and also to reduce the dimensionality of feature space. The proposed method is developed and analyzed using the publicly available databases DRIVE, IOSTAR and STARE.