Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spine fractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picture segmentation, feature reduction, and image classification. Two important elements are investigated to reduce the classification time: Using feature reduction software and leveraging the capabilities of sophisticated digital processing hardware. The researchers use different algorithms for picture enhancement, including the Wiener and Kalman filters, and they look into two background correction techniques. The article presents a technique for extracting textural features and evaluates three picture segmentation algorithms and three fractured spine detection algorithms using transform domain, Power Density Spectrum (PDS), and Higher-Order Statistics (HOS) for feature extraction. With an emphasis on reducing digital processing time, this all-encompassing method helps to create a simplified system for classifying fractured spine fractures. A feature reduction program code has been built to improve the processing speed for picture classification. Overall, the proposed approach shows great potential for significantly reducing classification time in clinical settings where time is critical. In comparison to other transform domains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and the process of extracting features from the transform domain took less time. More capable hardware can also result in quicker execution times for the feature extraction algorithms.