Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment

Abstract

Many common eye diseases and cardiovascular diseases can be diagnosed through retinal imaging. However, due to uneven illumination, image blurring, and low contrast, retinal images with poor quality are not useful for diagnosis, especially in automated image analyzing systems. Here we propose a new image enhancement method to improve color retinal image luminosity and contrast. Methods: A luminance gain matrix, which is obtained by gamma correction of the value channel in the HSV (Hue, Saturation, and Value) color space, is used to enhance the R, G, and B (Red, Green and Blue) channels, respectively. Contrast is then enhanced in the luminosity channel of L*a*b* color space by CLAHE (contrast limited adaptive histogram equalization). Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images

Existing System

Image Luminosity and Contrast Normalization Techniques, Multi-Scale Method Based on the Contourlet Transform, CLAHE (Contrast Limited Adaptive Histogram Equalization), Retinex-Based Enhancement Algorithm.

Proposed System

We propose an effective method for color retinal image enhancement based on luminosity and contrast adjustment. First, the luminosity of the color retinal image is enhanced by a luminance gain matrix based on gamma correction, and then image contrast is enhanced by CLAHE in the L*a*b* color space. The performance of our proposed method was validated on two large color retinal image datasets. The results show that, compared with contrast enhancement in other color spaces and other methods, our proposed method achieves superior improvement of color retinal images, especially for those with initially of poor quality. This method is not only able to enhance important anatomical structures of the retina, but it also preserves the naturalness of the images.

Architecture

BLOCK DIAGRAM