Dual-Filter X-Ray Image Enhancement Using Cream and Bosso Algorithms: Contrast and Entropy Optimization Across Anatomical Regions

This paper presents a dual-filter X-ray image enhancement technique that improves the quality of images of the knee, breast, and wrist using the Cream and Bosso algorithms. Quantitative analysis yields significant improvements in bone architecture, edge definition, and contrast (p < 0.001). The processing parameters are determined from the relationship between the entropy metrics and the filtering parameter d. The results yield contrast improvements of 45% for knee radiographs and 38% for wrist radiographs, with acceptable noise levels. It is compared with CLAHE techniques, unsharp masking, and deep learning-based models. This method provides a reliable and computationally efficient way to improve clinical diagnosis in resource-limited settings, improving robustness and interpretation.