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In this study, we propose a method for marker detection in X-ray fluoroscopy sequences based on adaptive thresholding and classification. Adaptive thresholding yields multiple marker candidates. To remove non-marker areas, 24 specific features are extracted from each extracted patch and four supervised classifiers are trained to differentiate non-marker areas from marker areas. Quantitative evaluation was carried out to assess different classifier performance by calculating accuracy, sensitivity, specificity and precision. SVM outperforms other classifiers based on the mean value for accuracy, specificity and precision with 81.56, 91.94 and 84.21%, respectively.

Automatic stent and catheter marker detection in X-ray fluoroscopy using adaptive thresholding and classification