Performance Analysis of Various Contrast Enhancement techniques with Illumination Equalization on Retinal Fundus Images

Retinal diseases are the source for blindness in human eyes. These diseases are diagnosed by examining the fundus images of the retina. People who are affected by eye diseases have different types of lesions on their retina and some abnormalities in the retinal blood vessels as well as in optic disc. An automatic computerized retinal disease detection system requires the retinal structures to be segmented properly. In order to do it, quality of the image is to be enhanced to eliminate the image acquisition issues so as to separate easily the dark and bright retinal structures from its background. This can be done through various contrast enhancement and illumination equalization techniques in the preprocessing steps. Hence, this paper analyzes the performance of three different contrast enhancement techniques without illumination equalization and with illumination equalization for retinal fundus images on three benchmark datasets namely, diaretdb1, drive and ROC. Mean Square Error and Peak Signal-Noise Ratio are the two performance metrics considered.