## 3 Results and Discussion
### Performance Evaluation  
The quality of the colorized medical pictures was evaluated using two key characteristics: peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These actions also enhance the colorized image and its appearance: the greater the PSNR score, the more visual information and better accuracy the colorized picture has. Results were assessed using the average PSNR values over the whole dataset. SSIM (the similarity between input and output image structures) is another aspect used. The average similarity measure is utilized to assess the process's systemic accuracy. If the SSIM was high, a structure exists between input and output images.  
Consequently, the measure tries to capture changes in people's perceptions of their photographs and quality. A database of 35 brain images was used during the simulation and performance evaluation. These images were obtained from the Bahawal Victoria MR images dataset images (16 images) and the Rider MR Image dataset (19 images). Figure 1 describes the Rider Image dataset, and Figure 2 describes the Bahawal Victoria MR Images dataset. These images were collected and preprocessed for the better performance of our algorithmic code. These images were processed using MATLAB R2018a. Analysis of the obtained images showed that it is too hard for an ordinary person to detect a brain tumor using these images.  
Brain tumor identification requires efficient area segmentation before the tumor can be identified. For this purpose, the watershed algorithm is used to segment the image area. This algorithm works using the concept of a catchment basin in a map using the height values. Imaging flow is detected until the last reached value, and then it is considered the end of that particular area. This concept is executed by grouping image pixels based on their intensities. A group of pixels is produced with similar intensity. This step is critical for its possible suffering from segmentation errors (under and over-segmentation). However, to avoid segmentation errors, the segmentation marker is chosen from the local minima of the image gradient. After choosing the local gradient minima as a marker, region merging is performed using this marker. Results after processing and evaluation of both datasets are shown below in Figures 6-9.  
Figure 6: Processing and results of BVH dataset. **(a)** Grayscale image; **(b)** segmentation; **(c)** morphological operations; **(d)** ROI selection and coloring; **(e)** differentiated tissue.  
Figure 7: Processing and results of an image from the Rider Neuro dataset. **(a)** Grayscale image; **(b)** segmentation; **(c)** morphological operations; **(d)** ROI selection and coloring; **(e)** differentiated tissue.  
Figure 8: Processing and results of BVH dataset. (**a**) Grayscale image; (**b**) segmentation; (**c**) morphological operations; (**d**) ROI selection and coloring; (**e**) differentiated tissue.  
Figure 9: Processing and results of an image from Rider Neuro dataset. (**a**) Grayscale image; (**b**) segmentation; (**c**) morphological operations; (**d**) ROI selection and coloring; (**e**) no tissue identified.