## 1 Introduction
### Related Works  
[PERSON]. [PERSON] et al. reported a colorization approach using an optimization technique to color available grayscale images. This approach is automated and does not need operator involvement. In this YUV color space used for applying colors, they colorized images and movie clips. They considered pixels to be a neighborhood that was timewise adjacent to space. This approach did not require precise segmentation and user interaction [20]. [PERSON] and [PERSON] proposed a semiautomated color-transfer approach to convert grayscale images and videos into colored versions. The proposed method was applied to still images and videos and compared with the automated segmentation method. This method requires human interaction, where a user indicates the desired \"color marker\" to each region. However, the automated segmentation method did not detect exact boundaries and tedious tasks [21]. [PERSON] et al. used pivoting, editing, zooming, and CLACHE evening out to maximize the available data. The use of k implies bunching, brain tumors were separated, and component foci were gathered. Support vector machine classifier (SVM) image order computation aids in accurately recognizing a tumor in its early stages. Their work demonstrated how to combine essential k implies grouping with dropout arrangement and SVM information contention to create a new viewpoint in the field of picture order, which was extensively studied [22]. [PERSON] and [PERSON] proposed a technique used for colorization by transferring color from a source image to an image [23]. The proposed method was inspired by the algorithm of [PERSON] et al. and worked based on the similarity image enhancing technique. The critical feature of this new technique was the adaption of efficient data and large data structures to retrieve \"color words\" from a vast vocabulary. This approach does not work on nonhomogeneous images [24]. [PERSON] et al. discussed the colorization of CT brain images. They introduced a technique based on a histogram to enhance the visualization of brain tumors to minimize human error. The pseudo-coloring method was used to color the abnormal regions of a brain, such as a tumor or an injury. The technique improved accuracy and time by 13.3% and 21.6%, respectively [25]. [PERSON] et al. proposed a colorization approach to grayscale medical images using user interaction to convert grayscale medical images into colored images and then convert the colored images again into grayscale images. Their presented method was applied to 40 X-ray, dental, and MRI images. This methodology does not work on CT images [26]. [PERSON] et al. discussed the two methods to increase the discrimination between normal and abnormal tissues using CIELAB color and space. The second method automatically segmented the three regions of a T2 brain MR image by using the approach of auto centroid selection. The auto centroid segmentation method efficiently separates gray and white matter, and cerebrospinal fluid (CSF). The proposed method achieved better segment results when compared with watershed and Gaussian classifier segmentation methods [27]. [PERSON] and [PERSON] used morphological distance transformation to assign colors using scribbling techniques on different medical images. Colors were automatically propagated in the different regions of brain MR images by following standard distance transform, hybrid distance transform, and color blending steps. The key feature was the preservation of the intensity of the original image. However, this method was unsuitable for coloring the available images [28]. [PERSON] et al. first selected an appropriate chromatic image for reference to colorize images. The grayscale and colored (reference) image was converted into a \(l\alpha\beta\) color space. Then, the best matching chromatic pixel for each pixel of the grayscale image was selected after this chromatic value had been transformed from the best matching reference pixel to the corresponding grayscale pixel.