Paper Title
Enhanced Radiological Visualization of Bone Fractures Using Advanced Image Processing Techniques: CLAHE, UNSHARP Masking, and High-Frequency Emphasis Filtering

Abstract
Among the most common clinical emergencies worldwide, bone fractures affect more than 178 million new cases per year and present an important challenge to accurate and prompt diagnosis due to the current limitations of X-ray radiography. Conventional X-ray films poorly reflect contrast and have poor definition of images; therefore, the estimated fracture detection rate is 10-15% with a high incidence of complications of malunion, nonunion and chronic disability. This paper covers these important diagnostic gaps by exploring three advanced image processing methods for improving bone fracture visual information in radiological images, namely, Contrast Limited Adaptive Histogram Equalization (CLAHE), Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Medical imaging, particularly x-ray radiography, remains the standard for preliminary diagnosis of fractures because of its ubiquity and cost effectiveness; particularly in the emergency department and in resource-poor settings where more advanced imaging modalities may not immediately be available. However, conventional X-ray imaging does have some inherent limitations which can limit the accurate diagnosis and treatment planning, especially for subtle fractures, hairline breaks and injuries in anatomically complex areas. The end goal of this study is to enhance the diagnostic quality in terms of contrast enhancement, edge enhancement, and noise reduction while preserving the anatomical details relevant to clinical decision making.These findings have important clinical significance. The following sharpening procedures are considered to be of diagnostic value: sharpening in tissue borders (CLAHE); sharpening edges facilitating rapid scanning of reports (Unsharp Masking); sharpening cracks in frequency domain (HEF); granular cracks. Hence, the summation of all these modalities can be very promising for a much better radiological visualisation with lower diagnostic errors and better patient outcome. From a healthcare system perspective, the use of such innovative processing techniques can result in significant cost savings by reducing the number of repeat imaging that needs to occur, reducing the number of redundant advanced imaging procedures, reducing the number of patient telephone calls to request repeat scans, etc. The paper concludes: systematic combination of such image processing techniques could markedly improve the quality of fracture diagnosis without sacrificing the computational feasibility for its wide clinical application. The authors recommend hybrid methods that combine complementary features of such point approaches in a synergistic way while being computationally efficient and easily ported into current clinician and hospital information system workflows.