Improved retinal fundus image quality with hybrid image filter and enhanced contrast limited adaptive histogram equalization

Abstract

Retinal fundus image preprocessing has contributed extensively to medical image analysis and retrieval of appropriate images. The acquired retinal images are usually messy and come from different sources. They need to be standardized and cleaned up. Retinal image preprocessing enables the improvement of retinal image quality and enhances the image features that are required for processing. The success of the retinal image diagnosis for early prediction of Diabetic Retinopathy (DR) depends on the reliability of preprocessing. The automatic preprocessing of color retinal fundus images without affecting the image quality is still challenging. Retinal images often have issues such as low contrast intensity, uneven light intensity, blurring, noise disturbances, sensor system that lack of focus, low contrast, irregular shapes with high variability, object movement, ill-defined boundaries, heterogeneous pixel intensities and the annotation of medical images to support diagnosis. In this work, issues such as noisy/ill in defined boundaries, uneven light intensity, low contrast, and blurring of images in the retinal fundus images are addressed with the proposed method. The results of the proposed method are reliable, contrast enhanced, edge preserved preprocessed images for given color input images.