Motion blur detection in radiographs

Hui Luo; William J. Sehnert; Jacquelyn S. Ellinwood; David Foos; Bruce Reiner M.D.; Eliot Siegel M.D.

doi:10.1117/12.770613

11 March 2008 Motion blur detection in radiographs

Hui Luo, William J. Sehnert, Jacquelyn S. Ellinwood, David Foos, Bruce Reiner M.D., Eliot Siegel M.D.

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69140U (2008) https://doi.org/10.1117/12.770613
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Image blur introduced by patient motion is one of the most frequently cited reasons for image rejection in radiographic diagnostic imaging. The goal of the present work is to provide an automated method for the detection of anatomical motion blur in digital radiographic images to help improve image quality and facilitate workflow in the radiology department. To achieve this goal, the method first reorients the image to a predetermined hanging protocol. Then it locates the primary anatomy in the radiograph and extracts the most indicative region for motion blur, i.e., the region of interest (ROI). The third step computes a set of motion-sensitive features from the extracted ROI. Finally, the extracted features are evaluated by using a classifier that has been trained to detect motion blur. Preliminary experiments show promising results with 86% detection sensitivity, 72% specificity, and an overall accuracy of 76%.

Citation Download Citation

Hui Luo, William J. Sehnert, Jacquelyn S. Ellinwood, David Foos, Bruce Reiner M.D., and Eliot Siegel M.D. "Motion blur detection in radiographs", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140U (11 March 2008); https://doi.org/10.1117/12.770613

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