Abstract

Retinal image characteristic is utilized for early diagnosis of diseases like hypertension, diabetes, glaucoma etc. Computerized diagnostic system can increase the effectiveness of large volume screening process of retinal images. For automated analysis of retinal fundus images, application of image enhancement techniques play a vital role. In this paper we have evaluated the effectiveness of four enhancement techniques which are prevalently used in this relevant field. These techniques are applications of contrast limited adaptive histogram equalization (CLAHE), adaptive gamma correction (AGC), morphological enhancement operation and Hessian matrix based ridge enhancement. Visual information based and statistical error based performance metrics are used as image quality assessment(IQA) index. These metrics are peak signal to noise ratio (PSNR), absolute mean brightness error (AMBE), structural similarity index (SSIM), correlation coefficient (CoC) and entropy (ENT). In terms of both types of performance metrics, application of morphological operation achieves the best performance quantitatively for both abnormal and normal retinal images.

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