Preprocessing of Retinal Fundus Image Using Clifford Algebra

This research presents a novel approach for enhancing retinal fundus images to detect anomalies better and diagnose retinal diseases. The work is divided into two stages: image representation and enhancement. Fundus images are represented in a Clifford color space, a 3D color model based on the RGB system, where colors are stored as multivectors that preserve color information and luminance. A rotation operation is applied to correct the image's illumination by adjusting brightness and color deviations, with the rotation angle and axis being critical for accurate enhancement. The gray-level axis serves as the rotational plane and the rotational angle of with a grayscale bivector axis, determined via discrete entropy (DE), optimally corrects image illumination. Following this, the green channel is extracted and enhanced using the CLAHE technique before being recombined with the other channels, and the image is reverse-rotated to its original color space. The effectiveness of the proposed method is evaluated using PSNR, DE, and SSIM on the MESSIDOR and DRIVE datasets, showing superior image quality and information preservation compared to existing methods. This enhanced technique is particularly beneficial for retinal landmark and lesion detection, improving diagnostic accuracy in retinal imaging.