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

作　　者：Fayez Alfayez 

机构地区：[1]Department of Computer Science and Information,College of Science,Majmaah University,AL-Majmaah,11952,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第4期1539-1560,共22页计算机、材料和连续体（英文）

基　　金：the appreciation to the Deanship of Postgraduate Studies and ScientificResearch atMajmaah University for funding this research work through the Project Number R-2024-922.

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

关 键 词：Feature reduction image classification X-ray images 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]