## 2 Literature Study  
In recent times, medical image processing has emerged as a captivating and highly relevant research area for scholars. Within this field, two key aspects have garnered considerable attention: image enhancement, and the identification of low-contrast issues [13]. Both of these areas hold significant importance in the realm of medical image processing. Image enhancement aims to improve the quality and visual clarity of medical images, while the identification of low-contrast problems plays a crucial role in identifying and addressing issues related to image quality and visibility [14]. A summary of the literature review is shown in Table 1.  
The current registration techniques fall short of preserving valuable information and image quality. To address this limitation and maintain the original image's features, the proposed method combines contrast enhancement with the registration process. In this new approach, a scanned medical image is taken as the starting point. To enhance image quality, a dynamic histogram equalization method is used [15]. The proposed method focuses on making medical images look better, rather than just improving technical measures like PSNR or the amount of data that can be hidden in them. They split the images into two parts: one with important details (Region of Interest) and one without. When it comes to medical images, it is crucial that details are highlighted in the less vibrant areas to help doctors diagnose effectively [16]. It is important to improve image quality as specialists find it difficult to analyze poor-quality images. To address this issue, a method called \"Dynamic Histogram Equalization with Modified Histogram Equalization on Fuzzy based Improved Particle Swarm Optimization (FIPSO)\" was developed. This approach aims to enhance the image's contrast, making it easier for specialists to examine. It achieves this by smoothing out the image details and distributing pixel intensity to nearby pixels using a Gaussian function [17]. The proposed approach aims to improve the contrast of medical images. The core concept revolves around creating an optimization challenge that takes into account both overall and specific enhancements in order to create a robust image enhancement method [18]. In [19], a new method called the \"adaptive fuzzy gray-level difference histogram equalization algorithm\" was proposed, in order to offer a more precise interpretation and cleaner images with less noise. Initially, the differences in gray levels in the input image were calculated using binary similar patterns. These gray-level differences were then made more flexible, in order to handle any uncertainties in the image. After this adjustment, a fuzzy gray-level difference clip limit was computed to control excessive contrast enhancement, ensuring that only relevant enhancements are made. In [20], an algorithm was introduced called \"piecewise linear histogram equalization (PLHE)\". This algorithm is a more advanced version of CHE, offering simplicity, a piecewise linear approach, and the convenience of a single parameter to manage the enhancement process. In order to demonstrate how well this new algorithm works, we have chosen two measures, information entropy and sharpness, that showcase the visual improvements it brings about. Another improved method for local contrast preservation is adaptive histogram equalization. This technique incorporates various image processing steps, including changing color spaces, inverting images, reducing haze, and boosting color saturation [21]. A chaos-based optimization algorithm has also been developed to enhance contrast in medical images. In this approach, a weighted combined framework was introduced as the method for improving contrast, serving as the cost function [22]. Several methods have been proposed to enhance the contrast and quality of medical images, addressing the limitations of current registration techniques. These approaches combine contrast enhancement with the registration process, aiming to preserve valuable image details.