# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 5 Discussion  
In modern ophthalmology, the application of fundus photography has been observed to be notable in the early screening of glaucoma, cataracts, and diabetic retinopathy. Further, it assists clinical experts in quickly analyzing the relationship between normal and intricate features of DR. Fundus images often suffer from a dynamic range of image artifacts such as noise, low contrast, and non-uniform illumination. Therefore, there is a dire need for image preprocessing steps to normalize and regulate the contrast of fundus images in order to obtain high segmentation and classification results [34; 35; 36; 37; 38]. In this paper, a novel medical fundus image enhancement scheme is presented based on a color perception model, named a CIECAM02 model, with a contrast enhancement method. To improve the global content and contrast of an image, an enhancement approach was applied using a CIECAM02 lightness \(J\) component in a nonlinear fashion. To quantitatively evaluate the performance of the proposed art, some quality parameters, i.e., PSNR, CNR, histogram, entropy, and SSIM, were applied to 1240 fundus images collected from two publicly available datasets. These parameters were widely adopted by the common histogram-based image enhancement arts such as HE, AHE, CLAHE, and ESIHE to measure image quality. The proposed method performance using the quality parameters in the experiments showed a significant improvement in fundus image quality enhancement over the other histogram methods. However, the performance of the ESIHE method was noticed to be far better than its other extensions in image enhancement. The first experiment was to compare the histogram of the proposed art with reference to the histogram of the input image and other histogram-based approaches, as shown in Figure 8. The \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Total Images**} & \multirow{2}{*}{**Method**} & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \cline{3-6}  & & **Entropy** & **PSNR** & **SSIM** & **CNR** \\ \hline \multirow{6}{*}{**1240 Images**} & Original & 4.61 \(\pm\) 4.55 & 9.77 \(\pm\) 6.55 & 1 \(\pm\) 0.99 & 3.99 \(\pm\) 3.70 \\  & HE & 4.31 \(\pm\) 4.11 & 10.78 \(\pm\) 7.04 & 0.96 \(\pm\) 0.90 & 5.65 \(\pm\) 4.10 \\ \cline{1-1}  & AHE & 4.23 \(\pm\) 4.00 & 13.89 \(\pm\) 10.11 & 0.96 \(\pm\) 0.91 & 5.79 \(\pm\) 4.11 \\ \cline{1-1}  & CLAHE & 5.12 \(\pm\) 4.63 & 15.80 \(\pm\) 13.89 & 0.96 \(\pm\) 0.92 & 7.01 \(\pm\) 5.99 \\ \cline{1-1}  & ESIHE & 5.00 \(\pm\) 4.42 & 19.90 \(\pm\) 15.90 & 0.97 \(\pm\) 0.94 & 7.90 \(\pm\) 6.90 \\ \cline{1-1}  & **Proposed** & **4.60 \(\pm\) 4.52** & **23.78 \(\pm\) 20.10** & **0.98 \(\pm\) 0.96** & **8.78 \(\pm\) 7.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation of the proposed method with histogram-related techniques based on mean and standard deviation of entropy, PSNR, SSIM, and CNR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**One-Way ANOVA** & **Entropy** & **PSNR** & **CNR** \\ \hline
**F-value** & 140.400 & 108.730 & 69.380 \\
**\(p\)-value** & 0.021 & 0.030 & 0.032 \\ \hline \hline \end{tabular} A \(p\)-value \(<\) 0.05 indicates statistical significance.  
\end{table} 
Table 7: Statistical analysis of proposed art with histogram-based methods using quality parameters. obtained histogram of the proposed method was notable in the adjustment of the histogram and contrast enhancement. The second experiment was to evaluate the entropy of the original images with the exploited enhancement methods. The term entropy means the average information of an original image (in this scenario, 4.56, 4.55, 4.57, and 4.61). As seen in Table 2, the obtained entropy of HE and AHE was less than the original image entropy value, which indicates the loss of image details and further leads to the production of false detection in DR subjects. Similarly, the entropy range of CLAHE far exceeded the original images. This indicates the enhancement of noise components.