## 6 Conclusions  
This research work provides a solution to the problems of using medical images such as retinal fundus color images and brain MRI for disease diagnosis. These images suffer from noise and low-varying contrast that impact the segmentation process for retinal blood vessel and brain tumor extraction. The first step required implementing the preprocessing  
Figure 11: Impact of the Proposed Enhancement Method on the Segmentation of Retinal Blood Vessels. The first column represents the color retinal fundus images, the second column represents the ground truth images, and the third column represents the segmented tumor image. Figure 12: Impact of the Proposed Enhancement Method on Brain Tumor Segmentation. The first column represents the MR brain image, the second column represents the ground truth image, and the third column represents the segmented tumor image. or image enhancement step. Many traditional techniques have been used to resolve noise and the low-varying contrast problem; however, there remained a requirement for a novel method based on the nature of the image. In this work, the McCann Retinex algorithm was used to solve the low-varying contrast of these two types of images. The iterative McCann Retinex algorithm was used as an improvement technique to speed up the postprocessing steps. In this research work, the impact of the iterative McCann Retinex algorithm was analyzed on the FAZ of retinal fundus images and MRI brain images. The optimal iteration was chosen according to the improvement in the PSNR and the contrast of the image. There were 20 iterations to analyze the performance on the HRF retinal image database and the Oasis brain MRI database. The main contribution of this research was the analysis of the low-varying-contrast enhancement on the two databases. It is observed that the brain MRI exhibited higher contrast and less noise, whereas the retinal fundus images exhibited noise issues as well as varying to low contrast; however, our challenge was to implement the algorithm for both databases for disease diagnosis. The proposed method improved the noise reduction and contrast in both databases, and it resulted in better image quality. This improvement provides another motivation to reduce the invasive method of FFA, because temporary injection-based FFAs improve contrast but impact patient health. However, this proposed method overcomes the problem of low-varying contrast, is safer, and facilitates the postprocessing steps to obtain a well-segmented image. This study used the High-Resolution Fundus (HRF) and Oasis databases. With our technique, the average PSNR enhancement on the Oasis database images was about 3 dB with an average contrast of 57.4. The average PSNR enhancement over the HRF database images was approximately 2.5 dB with a contrast average of 40 over the database. Such enhancement of contrast and noise level may be a better option for imaging well-segmented retinal blood vessels and accurate detection of brain tumors, as validated with comparable performance against existing methods. The proposed method was also validated in the postprocessing steps to observe its impact. The well-segmented image was obtained with an accuracy of 0.953 and 0.0949 on the DRIVE and STARE databases. The brain MRI was of good quality; however, due to the poor position of the patient, the image suffered from a lack of contrast between the white and gray matter of the MRI cerebral images, and it would be expensive to redo an MRI. The brain tumor was detected from the Oasis brain MRI database with an accuracy of 0.97. Preprocessing techniques are needed to overcome this problem and establish an accurate diagnosis of a brain tumor. Our image enhancement technique, along with the postprocessing steps, successfully segmented an accurate brain tumor. There is still room for improvement in this work, as this image enhancement technique can apply to other medical images such as breast cancer databases and ultrasound databases.