Nested approach for X-ray image enhancement based on contrast adaptive using histogram equalization

Abstract

Medical image enhancement is a topic of great interest to researchers due to the rapid evolution of technology and advancements in communication. There are many types of medical images such as X-ray images, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound images, positron emission tomography (PET) scans, single photon emission computed tomography (SPECT) scans, digital radiography images, mammography images and Fluoroscopy images. X-ray imaging is a valuable tool for diagnosis, monitoring, and treatment of many medical conditions, and its non-invasive, widely available, low cost and fast nature makes it a popular choice for many medical professionals. The proposed approach presents an algorithm for enhancing X-ray images, improving their visual appearance and making their content more useful and meaningful. The results of the algorithm show that enhanced images have a more natural look and provide accurate details of the objects in the X-ray images. Overall, this algorithm can aid in the diagnostic process by providing clearer and more detailed images for medical professionals to interpret.