Medical X-ray imaging is a fundamental pillar of musculoskeletal assessment, providing useful information on bone structure, alignment, and potential anomalies. However, such images are generally accompanied by noise, poor contrast, and a number of different types of artifacts, which can mask important anatomical detail and render accurate analysis impossible. These limitations not only affect diagnostic interpretation but also pose challenges to the design and optimization of ergonomic medical devices that rely on precise anatomical data to deliver personalized support. In this article, we review classic image-enhancement techniques alongside modern deep-learning approaches for improving the quality and interpretability of X-ray images. We propose an experimental design workflow that evaluates the performance of several augmentation methods towards the application in intelligent chair design. Further, we present an integrated system architecture that incorporates augmented imaging and AI-driven decision-making modules.
Finally, we consider both objective metrics and observer-rated evaluation criteria to evaluate the performance of the proposed strategies and their capacity to optimize patient care and device function.
Index Terms—X-ray enhancement, CLAHE, Bilateral Filter,Histogram Equalization, Log Correction, Adaptive Threshold,Gaussian Blur, AI, Smart Chair, IoT.

Auteurs : Ali Hamdi , Sawsan Selmi, Hédi Sakli

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