Performance analysis of diabetic retinopathy using diverse image enhancement techniques

ABSTRACT

Most of the retinal disease can be manifested by retinal fundus images. However, the fundus image quality is not adequate for diagnosing the retinal disease such as diabetic retinopathy (DR) due to colour distortion, low contrast, uneven illumination, and blurring. Therefore, there is a need for enhancing the images by applying various enhancement techniques. This paper implements diverse methods for image enhancement such as wiener filter, median filter, Contrast Stretching, Histogram Equalisation, Contrast adjustment, Morphological top hat filter, morphological bottom hat filter, Adaptive Histogram Equalisation, Contrast limited Adaptive histogram Equalisation (CLAHE) on retinal images. The performance has been evaluated on DRIVE and STARE dataset, which includes Peak Signal to noise Ratio, Structural similarity Index, Mean Square Error, Maximum difference, Normalised cross correlation, structural Content, Normalised absolute error, Average difference and Entropy of the images.

KEYWORDS:

• Diabetic retinopathy
• image enhancement
• diagnosis of DR
• filters
• denoising

Disclosure statement

No potential conflict of interest was reported by the author(s).