The process of generating pictorial representations of the inner human body for medical analysis and intervention is called medical imaging (MI). MI strives for revealing inner structures concealed by the existence of bones and skin to identify diseases in order to be treated properly. MI can be achieved through the use of various imaging systems, where each has different technology necessities. The output of these imaging systems is digital images that have meaningful medical information. Such images, however, do not own a high perceived quality due to the existence of image degradations. Despite modern technology advancement, various medical systems are still producing images with poor contrast owing to incorrect settings and device limitations. Such images must be processed efficiently to become clearer for better analysis, understanding and interpretation. In this study, a simple algorithm is developed, where it utilizes a combination of image processing concepts and statistical methods. The developed algorithm is appraised with a dataset of real-degraded low-contrast images only, assessed with one specialized no-reference metric and compared with four known contrast enhancement methods. From the conducted experiments, the proposed algorithm showed promising performances, since it produced acceptable-quality results rapidly and outperformed the comparison methods in different important aspects.
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL