Advancements in Image-Guided Surgical Procedures: Enhancing X-Ray Images, Cross Marker Classification, and C-Arm Rotation Detection

In this paper, we present a comprehensive software optimization methodology to enhance image-guided surgeries (IGS). Our approach integrates advanced techniques including Support Vector Machine (SVM) classification, improvement of low-frequency X-ray images, and precise detection of C-arm rotations. Utilizing SVM for marker differentiation, we achieve a marker classification accuracy of 96.5%, ensuring precise localization of surgical landmarks. To address the issue of low-frequency X-ray images, we implement enhancement techniques that improve image quality and clarity, aiding surgeons in decision-making. Additionally, our methodology incorporates a system for detecting minute C-arm rotations with high precision, facilitating the seamless integration of imaging data into the surgical workflow and optimizing real-time guidance for surgeons.