PROJECT TITLE :
Color Retinal Image Enhancement Based On Luminosity And Contrast Adjustment - 2017
Objective: 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 helpful for diagnosis, particularly in automated image analyzing systems. Here we have a tendency to propose a brand new image enhancement technique to improve color retinal image luminosity and contrast. Strategies: A luminance gain matrix, that is obtained by gamma correction of the value channel within the HSV (Hue, Saturation, and Worth) color space, is used to reinforce the R, G, and B (Red, Green and Blue) channels, respectively. Contrast is then enhanced within the luminosity channel of L*a*b* color space by CLAHE (distinction restricted adaptive histogram equalization). Image enhancement by the proposed method is compared to alternative strategies by evaluating quality innumerable the enhanced pictures. Results: The performance of the strategy is mainly validated on a dataset of 961 poor quality retinal pictures. Quality assessment (vary 0-1) of image enhancement of this poor dataset indicated that our method improved color retinal image quality from a mean of 0.0404 (standard deviation 0.0291) up to a median of 0.4565 (customary deviation 0.a thousand). Conclusion: The proposed method is shown to attain superior image enhancement compared to distinction enhancement in alternative color spaces or by other related strategies, whereas simultaneously preserving image naturalness. Significance: This technique of color retinal image enhancement may be employed to assist ophthalmologists in additional efficient screening of retinal diseases and in development of improved automated image analysis for clinical diagnosis.
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